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Semantic Inference for Question Answering. Sanda Harabagiu Department of Computer Science University of Texas at Dallas. Srini Narayanan International Computer Science Institute Berkeley, CA. and. Outline. Part I. Introduction: The need for Semantic Inference in QA

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Semantic inference for question answering l.jpg

Semantic Inference for Question Answering

Sanda Harabagiu

Department of Computer Science

University of Texas at Dallas

Srini Narayanan

International Computer Science Institute

Berkeley, CA

and


Outline l.jpg
Outline

  • Part I. Introduction: The need for Semantic Inference in QA

    • Current State-of-the-art in QA

    • Parsing with Predicate Argument Structures

    • Parsing with Semantic Frames

    • Special Text Relations

  • Part II. Extracting Semantic Relations from Questions and Texts

    • Knowledge-intensive techniques

    • Supervised and unsupervised techniques


Outline3 l.jpg
Outline

  • Part III. Knowledge representation and inference

    • Representing the semantics of answers

    • Extended WordNet and abductive inference

    • Intentional Structure and Probabilistic Metonymy

    • An example of Event Structure

    • Modeling relations, uncertainty and dynamics

    • Inference methods and their mapping to answer types


Outline4 l.jpg
Outline

  • Part IV. From Ontologies to Inference

    • From OWL to CPRM

    • FrameNet in OWL

    • FrameNet to CPRM mapping

  • Part V. Results of Event Structure Inference for QA

    • AnswerBank examples

    • Current results for Inference Type

    • Current results for Answer Structure


The need for semantic inference in qa l.jpg
The need for Semantic Inference in QA

  • Some questions are complex!

  • Example:

    • How can a biological weapons program be detected ?

    • Answer: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking at this murkiest and most dangerous corner of Saddam Hussein's armory. He says a series of reports add up to indications that Iraq may be trying to develop a new viral agent, possibly in underground laboratories at a military complex near Baghdad where Iraqis first chased away inspectors six years ago. A new assessment by the United Nations suggests Iraq still has chemical and biological weapons - as well as the rockets to deliver them to targets in other countries. The UN document says Iraq may have hidden a number of Scud missiles, as well as launchers and stocks of fuel. US intelligence believes Iraq still has stockpiles of chemical and biological weapons and guided missiles, which it hid from the UN inspectors.


Complex questions l.jpg
Complex questions

  • Example:

    • How can a biological weapons program be detected ?

  • This question is complex because:

    • It is a manner question

    • All other manner questions that were evaluated in TREC were asking about 3 things:

      • Manners to die, e.g. “How did Cleopatra die?”, “How did Einstein die?”

      • Manners to get a new name, e.g. “How did Cincinnati get its name?”

      • Manners to say something in another language, e.g. “How do you say house in Spanish?”

    • The answer does not contain any explicit manner of detection information, instead it talks about reports that give indications that Iraq may be trying to develop a new viral agent and assessments by the United Nations suggesting that Iraq still has chemical and biological weapons


Complex questions and semantic information l.jpg
Complex questions andsemantic information

  • Complex questions are not characterized only by a question class (e.g. manner questions)

    • Example: How can a biological weapons program be detected ?

    • Associated with the pattern “How can X be detected?”

    • And the topic X = “biological weapons program”

  • Processing complex questions is also based on access to the semantics of the question topic

    • The topic is modeled by a set of discriminating relations, e.g. Develop(program); Produce(biological weapons); Acquire(biological weapons) or stockpile(biological weapons)

    • Such relations are extracted from topic-relevant texts


Alternative semantic representations l.jpg
Alternative semantic representations

  • Using PropBank to access a 1 million word corpus annotated with predicate-argument structures.(www.cis.upenn.edu/~ace)

  • We can train a generative model for recognizing the arguments of each predicate in questions and in the candidate answers.

  • Example: How can a biological weapons program be detected ?

Predicate: detect

Argument 0 = detector : Answer(1)

Argument 1 = detected: biological weapons

Argument 2 = instrument : Answer(2)

Expected

Answer

Type


More predicate argument structures for questions l.jpg
More predicate-argument structures for questions

  • Example: From which country did North Korea import its missile launch pad metals?

  • Example:What stimulated India’s missile programs?

Predicate: import

Argument 0: (role = importer): North Korea

Argument 1: (role = commodity): missile launch pad metals

Argument 2 (role = exporter): ANSWER

Predicate: stimulate

Argument 0: (role = agent): ANSWER (part 1)

Argument 1: (role = thing increasing): India’s missile programs

Argument 2 (role = instrument): ANSWER (part 2)


Additional semantic resources l.jpg
Additional semantic resources

  • Using FrameNet

    • frame-semanticdescriptions of several thousand English lexical items with semantically annotated attestations (www.icsi.berkeley.edu/~framenet)

  • Example:What stimulated India’s missile programs?

  • Frame: STIMULATE

    Frame Element: CIRCUMSTANCES: ANSWER (part 1)

    Frame Element: EXPERIENCER: India’s missile programs

    Frame Element: STIMULUS: ANSWER (part 2)

    Frame: SUBJECT STIMULUS

    Frame Element: CIRCUMSTANCES: ANSWER (part 3)

    Frame Element: COMPARISON SET: ANSWER (part 4)

    Frame Element: EXPERIENCER: India’s missile programs

    Frame Element: PARAMETER: nuclear/biological proliferation


    Semantic inference for q a l.jpg
    Semantic inference for Q/A

    • The problem of classifying questions

      • E.g. “manner questions”:

      • Example“How did Hitler die?”

    • The problem of recognizing answer types/structures

      • Should “manner of death” by considered an answer type?

      • What other manner of event/action should be considered as answer types?

    • The problem of extracting/justifying/ generating answers to complex questions

      • Should we learn to extract “manner” relations?

      • What other types of relations should we consider?

      • Is relation recognition sufficient for answering complex questions? Is it necessary?


    Manner of death l.jpg
    Manner-of-death

    In previous TREC evaluations 31 questions asked about manner of death:

    • “How did Adolf Hitler die?”

  • State-of-the-art solution (LCC):

    • We considered “Manner-of-Death” as an answer type, pointing to a variety of verbs and nominalizations encoded in WordNet

    • We developed text mining techniques for identifying such information based on lexico-semantic patterns from WordNet

    • Example:

      • [kill #sense1 (verb) – CAUSE  die #sense1 (verb)]

  • Source of the troponyms of the [kill #sense1 (verb)] concept are candidates for the MANNER-OF-DEATH hierarchy

    • e.g., drown, poison, strangle, assassinate, shoot


  • Practical hurdle l.jpg

    X DIE in ACCIDENT seed: train, accident,

    be killed (ACCIDENT) car wreck

    X DIE {from|of} DISEASE seed: cancer

    be killed (DISEASE) AIDS

    X DIE after suffering MEDICAL seed: stroke,

    suffering of CONDITION (ACCIDENT) complications

    caused by diabetes

    Practical Hurdle

    • Not all MANNER-OF-DEATH concepts are lexicalized as a verb

       we set out to determine additional patterns that capture such cases

    • Goal: (1) set of patterns

      (2) dictionaries corresponding to such patterns

       well known IE technique: (IJCAI’99, Riloff&Jones)

    • Results: 100 patterns were discovered


    Outline14 l.jpg
    Outline seed: train, accident,

    • Part I. Introduction: The need for Semantic Inference in QA

      • Current State-of-the-art in QA

      • Parsing with Predicate Argument Structures

      • Parsing with Semantic Frames

      • Special Text Relations


    Slide15 l.jpg

    Answer types in seed: train, accident,

    State-of-the-art QA systems

    Ranked set

    of passages

    Docs

    Question

    Answer

    Question

    Expansion

    Answer

    Selection

    IR

    answer type

    Answer Type

    Prediction

    Answer Type Hierarchy

    Features

    • Answer type

      • Labels questions with answer type based on a taxonomy

      • Classifies questions (e.g. by using a maximum entropy model)


    In question answering two heads are better than one l.jpg
    In Question Answering two heads are better than one seed: train, accident,

    • The idea originated in the IBM’s PIQUANT project

      • Traditional Q/A systems employ a pipeline approach:

        • Questions analysis

        • Document/passage retrieval

        • Answer selection

      • Questions are classified based on the expected answer type

      • Answers are also selected based on the expected answer type, regardless of the question class

        Motivated by the success of ensemble methods in machine learning, use multiple classifiers to produce the final output for the ensemble made of multiple QA agents

    • A multi-strategy, multi-source approach.


    Multiple sources multiple agents l.jpg

    Question seed: train, accident,

    Analysis

    Answer

    Classification

    Multiple sources, multiple agents

    Knowledge Source

    Portal

    QGoals

    WordNet

    Q-Frame

    Answer Type

    Answering Agents

    QPlan

    Generator

    Cyk

    QUESTION

    Predictive Annot.

    Answering Agents

    Web

    Statistical

    Answering Agents

    Semantic

    Search

    Definitional Q.

    Answering Agents

    QPlan

    Executor

    KSP-Based

    Answering Agents

    Keyword

    Search

    Pattern-Based

    Answering Agents

    AQUAINT

    CNS

    TREC

    Answer Resolution

    Answers

    ANSWER


    Multiple strategies l.jpg
    Multiple Strategies seed: train, accident,

    • In PIQUANT, the answer resolution strategies consider that different combinations of the questions processing, passage retrieval and answer selection from different agents is ideal.

      • This entails the fact that all questions are processed depending on the questions class, not the question type

        • There are multiple question classes, e.g. “What” questions asking about people, “What” questions asking about products, etc.

        • There are only three types of questions that have been evaluated yet in systematic ways:

          • Factoid questions

          • Definition questions

          • List questions

    • Another options is to build an architecture in which question types are processed differently, and the semantic representations and inference mechanisms are adapted for each question type.


    The architecture of lcc s qa system l.jpg

    Document Processing seed: train, accident,

    Question Processing

    Factoid Answer Processing

    Single Factoid

    Passages

    Question Parse

    Answer Extraction

    Factoid

    Question

    Multiple

    List

    Passages

    Answer Justification

    Semantic

    Transformation

    Factoid

    Answer

    Answer Reranking

    Recognition of

    Expected

    Answer Type

    List

    Question

    Theorem Prover

    Axiomatic Knowledge Base

    Passage Retrieval

    Keyword Extraction

    Named Entity

    Recognition

    (CICERO LITE)

    Answer Type

    Hierarchy

    (WordNet)

    Answer Extraction

    List

    Answer

    List Answer Processing

    Document Index

    Threshold Cutoff

    Question Processing

    AQUAINT

    Document

    Collection

    Definition Answer Processing

    Definition

    Question

    Question Parse

    Answer Extraction

    Pattern Matching

    Pattern

    Repository

    Pattern Matching

    Keyword Extraction

    The Architecture of LCC’s QA System

    Multiple

    Definition

    Passages

    Definition

    Answer


    Extracting answers for factoid questions l.jpg
    Extracting Answers for seed: train, accident,Factoid Questions

    • In TREC 2003 the LCC QA system extracted 289 correct answers for factoid questions

    • The Name Entity Recognizer was responsible for 234 of them


    Special case of names l.jpg
    Special Case of Names seed: train, accident,

    Questions asking for names of authored works


    Ne driven qa l.jpg
    NE-driven QA seed: train, accident,

    • The results of the past 5 TREC evaluations of QA systems indicate that current state-of-the-art QA is determined by the recognition of Named Entities:

      • Precision of recognition

      • Coverage of name classes

      • Mapping into concept hierarchies

      • Participation into semantic relations (e.g. predicate-argument structures or frame semantics)


    Concept taxonomies l.jpg
    Concept Taxonomies seed: train, accident,

    • For 29% of questions the QA system relied on an off-line taxonomy with semantic classes such as:

      • Disease

      • Drugs

      • Colors

      • Insects

      • Games

    • The majority of these semantic classes are also associated with patterns that enable their identification


    Definition questions l.jpg
    Definition Questions seed: train, accident,

    • They asked about:

      • PEOPLE (most of them starting with “Who”)

      • other types of NAMES

      • general concepts

    • People questions

      • Many use the PERSON name in the format [First name, Last name]

        • examples: Aaron Copland, Allen Iverson, Albert Ghiorso

      • Some names had the PERSON name in format [First name, Last name1, Last name2]

        • example: Antonia Coello Novello

      • Other names had the name as a single word very well known person

        • examples: Nostradamus, Absalom, Abraham

      • Some questions referred to names of kings or princes:

        • examples: Vlad the Impaler, Akbar the Great


    Answering definition questions l.jpg
    Answering definition questions seed: train, accident,

    • Most QA systems use between 30-60 patterns

    • The most popular patterns:


    Complex questions26 l.jpg
    Complex questions seed: train, accident,

    • Characterized by the need of domain knowledge

    • There is no single answer type that can be identified, but rather an answer structure needs to be recognized

    • Answer selection becomes more complicated, since inference based on the semantics of the answer type needs to be activated

    • Complex questions need to be decomposed into a set of simpler questions


    Example of complex question l.jpg
    Example of Complex Question seed: train, accident,

    How have thefts impacted on the safety of Russia’s nuclear navy,

    and has the theft problem been increased or reduced over time?

    Need of domain knowledge

    To what degree do different thefts put nuclear

    or radioactive materials at risk?

    Question decomposition

    • Definition questions:

    • What is meant by nuclear navy?

    • What does ‘impact’ mean?

    • How does one define the increase or decrease of a problem?

      Factoid questions:

    • What is the number of thefts that are likely to be reported?

    • What sort of items have been stolen?

      Alternative questions:

    • What is meant by Russia? Only Russia, or also former Soviet

      facilities in non-Russian republics?


    The answer structure l.jpg
    The answer structure seed: train, accident,

    • For complex questions, the answer structure has a compositional semantics, comprising all the answer structures of each simpler question in which it is decomposed.

    • Example:

    Q-Sem: How can a biological weapons program be detected?

    Question pattern: How can X be detected? X = Biological Weapons Program

    Conceptual Schemas

    INSPECTION Schema

    Inspect, Scrutinize, Monitor, Detect, Evasion, Hide, Obfuscate

    POSSESSION Schema

    Acquire, Possess, Develop, Deliver

    Structure of Complex Answer Type:EVIDENCE

    CONTENT SOURCE QUALITY JUDGE RELIABILITY


    Answer selection l.jpg

    Conceptual Schemas seed: train, accident,

    INSPECTION Schema

    Inspect, Scrutinize, Monitor, Detect, Evasion, Hide, Obfuscate

    POSSESSION Schema

    Acquire, Possess, Develop, Deliver

    Answer Selection

    • Based on the answer structure

    • Example:

      • The CONTENT is selected based on:

      • Conceptual schemas are instantiated when predicate-argument structures or semantic frames are recognized in the text passages

      • The SOURCE is recognized when the content source is identified

      • The Quality of the Judgements, the Reliability of the judgements and the Judgements themselves are produced by an inference mechanism

    Structure of Complex Answer Type:EVIDENCE

    CONTENT SOURCE QUALITY JUDGE RELIABILITY


    Slide30 l.jpg

    ANSWER: Evidence-Combined: seed: train, accident,Pointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reports add up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Slide31 l.jpg

    Answer Structure (continued) seed: train, accident,

    ANSWER: Evidence-Combined:Pointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reports add up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    possess(Iraq, delivery systems(type : scud missiles; launchers; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium

    possess(Iraq, fuel stock(purpose: power launchers))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium

    hide(Iraq, Seeker: UN Inspectors; Hidden: CBW stockpiles & guided missiles)

    Justification: DETECTION SchemaInspection status: Past; Likelihood: Medium


    Slide32 l.jpg

    Answer Structure (continued) seed: train, accident,

    ANSWER: Evidence-Combined:Pointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reports add up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Source: UN documents, US intelligence

    SOURCE.Type: Assesment reports; Source.Reliability: Med-high Likelihood: Medium

    Judge: UN, US intelligence, Milton Leitenberg (Biological Weapons expert)

    JUDGE.Type: mixed; Judge.manner; Judge.stage: ongoing

    Quality: low-medium; Reliability: low-medium;


    State of the art qa learning surface text patterns l.jpg
    State-of-the-art QA: seed: train, accident,Learning surface text patterns

    • Pioneered by Ravichandran and Hovy (ACL-2002)

    • The idea is that given a specific answer type (e.g. Birth-Date), learn all surface patterns that enable the extraction of the answer from any text passage

      • Patterns are learned by two algorithms:

    • Relies on Web redundancy

    Algorithm 1 (Generates Patterns)

    Step 1: Select an answer type AT and a question Q(AT)

    Step 2: Generate a query (Q(AT) & AT) and submit it to

    search engine (google, altavista)

    Step 3: Download the first 1000 documents

    Step 4: Select only those sentences that contain the

    question content words and the AT

    Step 5: Pass the sentences through a suffix tree

    constructor

    Step 6: Extract only the longest matching sub-strings

    that contain the AT and the question word

    it is syntactically connected with.

    Algorithm 2 (Measures the Precision of Patterns)

    Step 1: Query by using only question Q(AT)

    Step 2: Download the first 1000 documents

    Step 3: Select only those sentences that contain

    the question word connected to the AT

    Step 4: Compute C(a)= #patterns matched

    by the correct answer;

    C(0)=#patterns matched by any word

    Step 6: The precision of a pattern is given by:

    C(a)/C(0)

    Step 7: Retain only patterns matching >5 examples


    Results and problems l.jpg
    Results and Problems seed: train, accident,

    • Some results:

    • Limitations:

      • Cannot handle long-distance dependencies

      • Cannot recognize paraphrases – since no semantic knowledge is associated with these patterns (unlike patterns used in Information Extraction)

      • Cannot recognize a paraphrased questions

    Answer Type=INVENTOR:

    <ANSWER> invents <NAME>

    the <NAME> was invented by <ANSWER>

    <ANSWER>’s invention of the <NAME>

    <ANSWER>’s <NAME> was

    <NAME>, invented by <ANSWER>

    That <ANSWER>’s <NAME>

    Answer Type=BIRTH-YEAR:

    <NAME> (<ANSWER>- )

    <NAME> was born on <ANSWER>

    <NAME> was born in <ANSWER>

    born in <ANSWER>, <NAME>

    Of <NAME>, (<ANSWER>


    Shallow semantic parsing l.jpg
    Shallow semantic parsing seed: train, accident,

    • Part of the problems can be solved by using shallow semantic parsers

      • Parsers that use shallow semantics encoded as either predicate-argument structures or semantic frames

        • Long-distance dependencies are captured

        • Paraphrases can be recognized by mapping on IE architectures

      • In the past 4 years, several models for training such parsers have emerged

      • Lexico-Semantic resources are available (e.g PropBank, FrameNet)

      • Several evaluations measure the performance of such parsers (e.g. SENSEVAL, CoNNL)


    Outline36 l.jpg
    Outline seed: train, accident,

    • Part I. Introduction: The need for Semantic Inference in QA

      • Current State-of-the-art in QA

      • Parsing with Predicate Argument Structures

      • Parsing with Semantic Frames

      • Special Text Relations


    Proposition bank overview l.jpg

    S seed: train, accident,

    NP

    VP

    VP

    PP

    NP

    The futures halt

    was

    assailed

    by

    Big Board floor traders

    ARG1 = entity assailed

    PRED

    ARG0 = agent

    Proposition Bank Overview

    • A one million word corpus annotated with predicate argument structures [Kingsbury, 2002]. Currently only predicates lexicalized by verbs.

    • Numbered arguments from 0 to 5. Typically ARG0 = agent, ARG1 = direct object or theme, ARG2 = indirect object, benefactive, or instrument.

    • Functional tags: ARMG-LOC = locative, ARGM-TMP = temporal, ARGM-DIR = direction.


    The model l.jpg

    S seed: train, accident,

    NP

    VP

    VP

    PP

    NP

    Task 1

    The futures halt

    was

    assailed

    by

    Big Board floor traders

    PRED

    ARG1

    ARG0

    Task 2

    The Model

    • Consists of two tasks: (1) identifying parse tree constituents corresponding to predicate arguments, and (2) assigning a role to each argument constituent.

    • Both tasks modeled using C5.0 decision tree learning, and two sets of features: Feature Set 1 adapted from [Gildea and Jurafsky, 2002], and Feature Set 2, novel set of semantic and syntactic features [Surdeanu, Harabagiu et al, 2003].


    Feature set 1 l.jpg

    PHRASE TYPE (pt): type of the syntactic phrase as argument. E.g. NP for ARG1.

    PARSE TREE PATH (path): path between argument and predicate. E.g. NP  S  VP  VP for ARG1.

    PATH LENGTH (pathLen): number of labels stored in the predicate-argument path. E.g. 4 for ARG1.

    POSITION (pos): indicates if constituent appears before predicate in sentence. E.g. true for ARG1 and false for ARG2.

    VOICE (voice): predicate voice (active or passive). E.g. passive for PRED.

    HEAD WORD (hw): head word of the evaluated phrase. E.g. “halt” for ARG1.

    GOVERNING CATEGORY (gov): indicates if an NP is dominated by a S phrase or a VP phrase. E.g. S for ARG1, VP for ARG0.

    PREDICATE WORD: the verb with morphological information preserved (verb), and the verb normalized to lower case and infinitive form (lemma). E.g. for PRED verb is “assailed”, lemma is “assail”.

    S

    NP

    VP

    VP

    PP

    NP

    The futures halt

    was

    assailed

    by

    Big Board floor traders

    ARG1

    PRED

    ARG0

    Feature Set 1


    Observations about feature set 1 l.jpg

    PP E.g. NP for ARG1.

    in

    NP

    last

    June

    SBAR

    that

    S

    VP

    occurred

    NP

    yesterday

    VP

    to

    VP

    be

    VP

    declared

    Observations about Feature Set 1

    • Because most of the argument constituents are prepositional attachments (PP) and relative clauses (SBAR), often the head word (hw) is not the most informative word in the phrase.

    • Due to its strong lexicalization, the model suffers from data sparsity. E.g. hw used < 3%. The problem can be addressed with a back-off model from words to part of speech tags.

    • The features in set 1 capture only syntactic information, even though semantic information like named-entity tags should help. For example, ARGM-TMP typically contains DATE entities, and ARGM-LOC includes LOCATION named entities.

    • Feature set 1 does not capture predicates lexicalized by phrasal verbs, e.g. “put up”.


    Feature set 2 1 2 l.jpg
    Feature Set 2 (1/2) E.g. NP for ARG1.

    • CONTENT WORD (cw): lexicalized feature that selects an informative word from the constituent, other than the head. Selection heuristics available in the paper. E.g. “June” for the phrase “in last June”.

    • PART OF SPEECH OF CONTENT WORD (cPos): part of speech tag of the content word. E.g. NNP for the phrase “in last June”.

    • PART OF SPEECH OF HEAD WORD (hPos): part of speech tag of the head word. E.g. NN for the phrase “the futures halt”.

    • NAMED ENTITY CLASS OF CONTENT WORD (cNE): The class of the named entity that includes the content word. 7 named entity classes (from the MUC-7 specification) covered. E.g. DATE for “in last June”.


    Feature set 2 2 2 l.jpg
    Feature Set 2 (2/2) E.g. NP for ARG1.

    • BOOLEAN NAMED ENTITY FLAGS: set of features that indicate if a named entity is included at any position in the phrase:

      • neOrganization: set to true if an organization name is recognized in the phrase.

      • neLocation: set to true if a location name is recognized in the phrase.

      • nePerson: set to true if a person name is recognized in the phrase.

      • neMoney: set to true if a currency expression is recognized in the phrase.

      • nePercent: set to true if a percentage expression is recognized in the phrase.

      • neTime: set to true if a time of day expression is recognized in the phrase.

      • neDate: set to true if a date temporal expression is recognized in the phrase.

    • PHRASAL VERB COLLOCATIONS: set of two features that capture information about phrasal verbs:

      • pvcSum: the frequency with which a verb is immediately followed by any preposition or particle.

      • pvcMax: the frequency with which a verb is followed by its predominant preposition or particle.


    Results l.jpg
    Results E.g. NP for ARG1.


    Other parsers based on propbank l.jpg
    Other parsers based on PropBank E.g. NP for ARG1.

    • Pradhan, Ward et al, 2004 (HLT/NAACL+J of ML) report on a parser trained with SVMs which obtains F1-score=90.4% for Argument classification and 80.8% for detecting the boundaries and classifying the arguments, when only the first set of features is used.

    • Gildea and Hockenmaier (2003) use features extracted from Combinatory Categorial Grammar (CCG). The F1-measure obtained is 80%

    • Chen and Rambow (2003) use syntactic and semantic features extracted from a Tree Adjoining Grammar (TAG) and report an F1-measure of 93.5% for the core arguments

    • Pradhan, Ward et al, use a set of 12 new features and obtain and F1-score of 93.8% for argument classification and 86.7 for argument detection and classification


    Applying predicate argument structures to qa l.jpg

    Q E.g. NP for ARG1.: What kind of materials were stolen from the Russian navy?

    PAS(Q): What [Arg1: kind of nuclear materials] were [Predicate:stolen]

    [Arg2: from the Russian Navy]?

    Applying Predicate-Argument Structures to QA

    • Parsing Questions

    • Parsing Answers

    • Result: exact answer= “approximately 7 kg of HEU”

    A(Q): Russia’s Pacific Fleet has also fallen prey to nuclear theft; in 1/96, approximately 7 kg of HEU was reportedly stolen from a naval base in Sovetskaya Gavan.

    PAS(A(Q)): [Arg1(P1redicate 1): Russia’s Pacific Fleet] has [ArgM-Dis(Predicate 1) also]

    [Predicate 1: fallen] [Arg1(Predicate 1): prey to nuclear theft];

    [ArgM-TMP(Predicate 2): in 1/96], [Arg1(Predicate 2): approximately 7 kg of HEU]

    was [ArgM-ADV(Predicate 2) reportedly] [Predicate 2: stolen]

    [Arg2(Predicate 2): from a naval base] [Arg3(Predicate 2): in Sovetskawa Gavan]


    Outline46 l.jpg
    Outline E.g. NP for ARG1.

    • Part I. Introduction: The need for Semantic Inference in QA

      • Current State-of-the-art in QA

      • Parsing with Predicate Argument Structures

      • Parsing with Semantic Frames

      • Special Text Relations


    The model47 l.jpg
    The Model E.g. NP for ARG1.

    • Consists of two tasks: (1) identifying parse tree constituents corresponding to frame elements, and (2) assigning a semantic role to each frame element.

    • Both tasks introduced for the first time by Gildea and Jurafsky in 2000. It uses the Feature Set 1 , which later Gildea and Palmer used for parsing based on PropBank.

    S

    NP

    VP

    NP

    PP

    Task 1

    She

    clapped

    her hands

    in inspiration

    PRED

    Agent

    Body Part

    Cause

    Task 2


    Extensions l.jpg
    Extensions E.g. NP for ARG1.

    • Fleischman et al extend the model in 2003 in three ways:

      • Adopt a maximum entropy framework for learning a more accurate classification model.

      • Include features that look at previous tags and use previous tag information to find the highest probability for the semantic role sequence of any given sentence.

      • Examine sentence-level patterns that exploit more global information in order to classify frame elements.


    Applying frame structures to qa l.jpg

    Q E.g. NP for ARG1.: What kind of materials were stolen from the Russian navy?

    FS(Q): What [GOODS: kind of nuclear materials] were [Target-Predicate:stolen]

    [VICTIM: from the Russian Navy]?

    Applying Frame Structures to QA

    • Parsing Questions

    • Parsing Answers

    • Result: exact answer= “approximately 7 kg of HEU”

    A(Q): Russia’s Pacific Fleet has also fallen prey to nuclear theft; in 1/96, approximately 7 kg of HEU was reportedly stolen from a naval base in Sovetskaya Gavan.

    FS(A(Q)): [VICTIM(P1): Russia’s Pacific Fleet] has also fallen prey to [Goods(P1): nuclear ]

    [Target-Predicate(P1): theft]; in 1/96, [GOODS(P2): approximately 7 kg of HEU]

    was reportedly [Target-Predicate (P2): stolen]

    [VICTIM (P2): from a naval base] [SOURCE(P2): in Sovetskawa Gavan]


    Outline50 l.jpg
    Outline E.g. NP for ARG1.

    • Part I. Introduction: The need for Semantic Inference in QA

      • Current State-of-the-art in QA

      • Parsing with Predicate Argument Structures

      • Parsing with Semantic Frames

      • Special Text Relations


    Additional types of relations l.jpg
    Additional types of relations E.g. NP for ARG1.

    • Temporal relations

      • TERQUAS ARDA Workshop

    • Causal relations

    • Evidential relations

    • Part-whole relations


    Temporal relations in qa l.jpg
    Temporal relations in QA E.g. NP for ARG1.

    • Results of the workshop are accessible from http://www.cs.brandeis.edu/~jamesp/arda/time/documentation/TimeML-use-in-qa-v1.0.pdf

    • A set of questions that require the extraction of temporal relations was created (TimeML question corpus)

      • E.g.:

        • “When did the war between Iran and Iraq end?”

        • “Who was Secretary of Defense during the Golf War?”

    • A number of features of these questions were identified and annotated

      • E.g.:

        • Number of TEMPEX relations in the question

        • Volatility of the question (how often does the answer change)

        • Reference to repetitive events

        • Number of events mentioned in the question


    Outline53 l.jpg
    Outline E.g. NP for ARG1.

    • Part II. Extracting Semantic Relations from Questions and Texts

      • Knowledge-intensive techniques

      • Unsupervised techniques


    Information extraction from texts l.jpg
    Information Extraction E.g. NP for ARG1.from texts

    • Extracting semantic relations from questions and texts can be solved by adapting the IE technology to this new task.

    • What is Information Extraction (IE) ?

      • The task of finding facts about a specified class of events from free text

      • Filling a table in a database with the information – sush a database entry can be seen as a list of slots of a template

    • Events are instances comprising many relations that span multiple arguments


    Ie architecture overview l.jpg

    Rules E.g. NP for ARG1.

    Entity coreference

    Rules

    Domain event rules

    Coreference filters

    Domain coreference

    Merge condition

    Templette merging

    IE Architecture Overview

    Phrasal parser

    Domain API


    Walk through example l.jpg

    Parser E.g. NP for ARG1.

    ... a bomb rigged with a trip wire/NGthat/Pexploded/VGand/Pkilled/VGhim/NG...

    Entity Coref

    him  A Chinese restaurant chef

    Domain Rules

    ... a bomb rigged with a trip wire that exploded/PATTERN and killed him/PATTERN...

    TEMPLETTE

    BOMB: “a bomb rigged with a trip wire”

    TEMPLETTE

    DEAD: “A Chinese restaurant chef”

    Domain Coref

    TEMPLETTE

    BOMB: “a bomb rigged with a trip wire”

    DEAD: “A Chinese restaurant chef”

    TEMPLETTE

    BOMB: “a bomb rigged with a trip wire”

    LOCATION: “MIAMI”

    Merging

    TEMPLETTE

    BOMB: “a bomb rigged with a trip wire”

    DEAD: “A Chinese restaurant chef”

    LOCATION: “MIAMI”

    Walk-through Example

    ... a bomb rigged with a trip wire that exploded and killed him...


    Learning domain event rules and domain relations l.jpg
    Learning domain event rules E.g. NP for ARG1.and domain relations

    • build patterns from examples

      • Yangarber ‘97

    • generalize from multiple examples: annotated text

      • Crystal, Whisk (Soderland), Rapier (Califf)

    • active learning: reduce annotation

      • Soderland ‘99, Califf ‘99

    • learning from corpus with relevance judgements

      • Riloff ‘96, ‘99

    • co-learning/bootstrapping

      • Brin ‘98, Agichtein ‘00


    Changes in ie architecture for enabling the extraction of semantic relations l.jpg
    Changes in IE architecture for enabling the extraction of semantic relations

    Document

    Tokenizer

    • Addition of Relation Layer

    • Modification of NE and

    • pronominal coreference

    • to enable relation coreference

    • Add a relation merging

    • layer

    Entity

    Coreference

    Entity

    Recognizer

    Event

    Recognizer

    Relation

    Recognizer

    Event/Relation

    Coreference

    Relation Merging

    EEML File

    Generation

    EEML Results


    Walk through example59 l.jpg

    Entity: Person semantic relations

    Entity: Person

    Entity: Person

    Entity: City

    Entity: Time-Quantity

    Entity: Person

    Entity: GeopoliticalEntity

    Walk-through Example

    Event: Murder

    The murder of Vladimir Golovlyov, an associate of the exiled

    tycoon Boris Berezovsky, was the second contract killing in

    the Russian capital in as many days and capped a week of

    setbacks for the Russian leader.

    Event: Murder


    Walk through example60 l.jpg
    Walk-through Example semantic relations

    Event-Entity Relation: Victim

    Entity-Entity Relation: AffiliatedWith

    The murder of Vladimir Golovlyov, an associate of the exiled

    tycoon Boris Berezovsky, was the second contract killing in

    the Russian capital in as many days and capped a week of

    setbacks for the Russian leader.

    Event-Entity Relation: Victim

    Event-Entity Relation: EventOccurAt

    Entity-Entity Relation: GeographicalSubregion

    Entity-Entity Relation: hasLeader


    Application to qa l.jpg
    Application to QA semantic relations

    • Who was murdered in Moscow this week?

      • Relations: EventOccuredAt + Victim

    • Name some associates of Vladimir Golovlyov.

      • Relations: AffiliatedWith

    • How did Vladimir Golovlyov die?

      • Relations: Victim

    • What is the relation between Vladimir Golovlyov and Boris Berezovsky?

      • Relations: AffliliatedWith


    Outline62 l.jpg
    Outline semantic relations

    • Part II. Extracting Semantic Relations from Questions and Texts

      • Knowledge-intensive techniques

      • Unsupervised techniques


    Learning extraction rules and semantic lexicons l.jpg

    Generating Extraction Patterns semantic relations: AutoSlog (Riloff 1993), AutoSlog-Ts(Riloff 1996)

    Semantic Lexicon Induction: Riloff & Shepherd (1997), Roark & Charniak (1998), Ge, Hale, & Charniak (1998), Caraballo (1999), Thompson & Mooney (1999), Meta-Bootstrapping (Riloff & Jones 1999), (Thelen and Riloff 2002)

    Bootstrapping/Co-training: Yarowsky (1995), Blum and Mitchell (1998), McCallum & Nigam (1998)

    Learning extraction rules and semantic lexicons


    Generating extraction rules l.jpg
    Generating extraction rules semantic relations

    • From untagged text: AutoSlog-TS (Riloff 1996)

    • The rule relevance is measured by:

      Relevance rate * log2 (frequency)

    STAGE 1

    Pre-classified Texts

    Concept Nodes:

    <x> was bombed

    by <y>

    Subject: World Trade Center

    Verb: was bombed

    PP: by terrorists

    Sentence

    Analyzer

    AutoSlog

    Heuristics

    STAGE 2

    Concept Nodes: REL%

    <x> was bombed 87%

    bombed by <y> 84%

    <w> was killed 63%

    <z> saw 49%

    Pre-classified Texts

    Concept Node

    Dictionary:

    <x> was killed

    <x> was bombed by <y>

    Sentence

    Analyzer


    Learning dictionaries for ie with mutual bootrapping l.jpg
    Learning Dictionaries for IE with mutual bootrapping semantic relations

    • Riloff and Jones (1999)

    Generate all candidate extraction rules rom the training corpus using AutoSlog

    Apply the candidate extraction rules to the training corpus and save the patterns

    With their extractions to EPdata

    SemLEx = {seed words}

    Cat_EPlist = {}

    • MUTUAL BOOTSTRAPPING LOOP

    • Score all extraction rules in Epdata

    • best_EP = the highest scoring extraction pattern not already in Cat_Eplist

    • Add best_EP to Cat_Eplist

    • Add best_EP’s extraction to SemLEx

    • Go to step 1.


    The basilisk approach thelen riloff l.jpg

    seed semantic relations

    words

    extraction patterns and

    their extractions

    Pattern Pool

    Candidate

    Word Pool

    The BASILISK approach (Thelen & Riloff)

    BASILISK= Bootstrapping Approach to SemantIc

    Lexicon Induction using Semantic Knowledge

    corpus

    Key ideas:

    1/ Collective evidence over a large set

    of extraction patterns can reveal strong

    semantic associations.

    2/ Learning multiple categories

    simultaneously can constrain the

    bootstrapping process

    best patterns

    extractions

    semantic

    lexicon

    5 best candidate words


    Learning multiple categories simultaneously l.jpg
    Learning Multiple Categories Simultaneously semantic relations

    • “One Sense per Domain” assumption: a word belongs to a single semantic category within a limited domain.

    • The simplest way to take advantage of multiple categories is to resolve conflicts when they arise.

    • 1. A word cannot be assigned to category X if it has already been assigned to category Y.

    • 2. If a word is hypothesized for both category X and category Y at the same time, choose the category that receives the highest score.

    Bootstrapping multiple categories

    Bootstrapping a single category


    Kernel methods for relation extraction l.jpg
    Kernel Methods for semantic relations Relation Extraction

    • Pioneered by Zelenko, Aone and Richardella (2002)

    • Uses Support Vector Machines and the Voted Perceptron Alorithm (Freund and Shapire, 1999)

    • It operates on the shallow parses of texts, by using two functions:

      • A matching function between the nodes of the shallow parse tree; and

      • A similarity function between the nodes

    • It obtains very high F1-score values for relation extraction (86.8%)


    Outline69 l.jpg
    Outline semantic relations

    • Part III. Knowledge representation and inference

      • Representing the semantics of answers

      • Extended WordNet and abductive inference

      • Intentional Structure and Probabilistic Metonymy

      • An example of Event Structure

      • Modeling relations, uncertainty and dynamics

      • Inference methods and their mapping to answer types


    Three representations l.jpg
    Three representations semantic relations

    • A taxonomy of answer types in which Named Entity Classes are also mapped.

    • A complex structure that results from schema instantiations

    • Answer type generated by the inference on the semantic structures


    Possible answer types l.jpg

    clock time semantic relations

    institution,

    establishment

    team,

    squad

    time of day

    prime time

    financial

    institution

    educational

    institution

    hockey

    team

    midnight

    integer,

    whole number

    distance,

    length

    width,

    breadth

    numerosity,

    multiplicity

    thickness

    population

    denominator

    wingspan

    altitude

    Possible Answer Types

    TOP

    PERSON LOCATION DATE TIME PRODUCT NUMERICAL MONEY ORGANIZATION MANNER REASON

    VALUE

    DEGREE DIMENSION RATE DURATION PERCENTAGE COUNT


    Examples l.jpg

    TOP semantic relations

    PERSON LOCATION DATE TIME PRODUCT NUMERICAL MONEY ORGANIZATION MANNER REASON

    VALUE

    What

    What

    produce

    name

    actress

    company

    played

    BMW

    Shine

    What is the name of the

    actress that played in Shine?

    What does the BMW company

    produce?

    Examples

    PERSON

    PRODUCT

    PERSON

    PRODUCT


    Outline73 l.jpg
    Outline semantic relations

    • Part III. Knowledge representation and inference

      • Representing the semantics of answers

      • Extended WordNet and abductive inference

      • Intentional Structure and Probabilistic Metonymy

      • An example of Event Structure

      • Modeling relations, uncertainty and dynamics

      • Inference methods and their mapping to answer types


    Extended wordnet l.jpg
    Extended WordNet semantic relations

    • eXtended WordNet is an ongoing project at the Human Language Technology Research Institute, University of Texas at Dallas. http://xwn.hlt.utdallas.edu/)

    • The goal of this project is to develop a tool that takes as input the current or future versions of WordNet and automatically generates an eXtended WordNet that provides several important enhancements intended to remedy the present limitations of WordNet.

    • In the eXtended WordNet the WordNet glosses are syntactically parsed, transformed into logic forms and content words are semantically disambiguated.


    Logic abduction l.jpg
    Logic Abduction semantic relations

    • Motivation:

    • Goes beyond keyword based justification by capturing:

      • syntax based relationships

      • links between concepts in the question and the candidateanswers

    Justification

    QLF

    ALF

    XWN axioms

    NLP axioms

    Lexical chains

    Axiom

    Builder

    Answer

    Ranking

    Ranked

    answers

    Success

    Answer

    explanation

    Proof

    fails

    Relaxation


    Cogex the lcc logic prover for qa l.jpg
    COGEX= the LCC Logic Prover for QA semantic relations

    Inputs to the Logic Prover

    A logic form provides a mapping of the question and candidate answer text into first order logic predicates.

    Question:

    Where did bin Laden 's funding come from other than his own wealth ?

    Question Logic Form:

    ( _multi_AT(x1) ) & bin_NN_1(x2) & Laden_NN(x3) & _s_POS(x5,x4) & nn_NNC(x4,x2,x3) & funding_NN_1(x5) & come_VB_1(e1,x5,x11) & from_IN(e1,x1) & other_than_JJ_1(x6) & his_PRP_(x6,x4) & own_JJ_1(x6) & wealth_NN_1(x6)


    Justifying the answer l.jpg
    Justifying the answer semantic relations

    Answer:

    ... Bin Laden reportedly sent representatives to Afghanistan opium farmers to buy large amounts of opium , probably to raise funds for al - Qaida ....

    Answer Logic Form:

    … Bin_NN(x14) & Laden_NN(x15) & nn_NNC(x16,x14,x15) & reportedly_RB_1(e2) & send_VB_1(e2,x16,x17) & representative_NN_1(x17) & to_TO(e2,x21) & Afghanistan_NN_1(x18) & opium_NN_1(x19) & farmer_NN_1(x20) & nn_NNC(x21,x19,x20) & buy_VB_5(e3,x17,x22) & large_JJ_1(x22) & amount_NN_1(x22) & of_IN(x22,x23) & opium_NN_1(x23) & probably_RB_1(e4) & raise_VB_1(e4,x22,x24) & funds_NN_2(x24) & for_IN(x24,x26) & al_NN_1(x25) & Qaida_NN(x26) ...


    Lexical chains l.jpg
    Lexical Chains semantic relations

    Lexical Chains

    Lexical chains provide an improved source of world knowledge by supplying the Logic Prover with much needed axioms to link question keywords with answer concepts.

    Question:

    How were biological agents acquired by bin Laden?

    Answer:

    On 8 July 1998 , the Italian newspaper Corriere della Serra indicated that members of The World Front for Fighting Jews and Crusaders , which was founded by Bin Laden , purchasedthree chemical and biological_agent production facilities in

    Lexical Chain:

    ( v - buy#1, purchase#1 ) HYPERNYM ( v - get#1, acquire#1 )


    Axiom selection l.jpg
    Axiom selection semantic relations

    XWN Axioms

    Another source of world knowledge is a general purpose knowledge base of more than 50,000 parsed and disambiguated glosses that are transformed into logic form for use during the course of a proof.

    Gloss:

    Kill is to cause to die

    GLF:

    kill_VB_1(e1,x1,x2) -> cause_VB_1(e1,x1,x3) & to_TO(e1,e2) & die_VB_1(e2,x2,x4)


    Logic prover l.jpg
    Logic Prover semantic relations

    Axiom Selection

    Lexical chains and the XWN knowledge base work together to select and generate the axioms needed for a successful proof when all the keywords in the questions are not found in the answer.

    Question:

    How did Adolf Hitler die?

    Answer:

    … Adolf Hitler committed suicide …

    The following Lexical Chain is detected:

    ( n - suicide#1, self-destruction#1, self-annihilation#1 ) GLOSS ( v - kill#1 ) GLOSS ( v - die#1, decease#1, perish#1, go#17, exit#3, pass_away#1, expire#2, pass#25 ) 2

    The following axioms are loaded into the Usable List of the Prover:

    exists x2 all e1 x1 (suicide_nn(x1) -> act_nn(x1) & of_in(x1,e1) & kill_vb(e1,x2,x2)).

    exists x3 x4 all e2 x1 x2 (kill_vb(e2,x1,x2) -> cause_vb_2(e1,x1,x3) & to_to(e1,e2) & die_vb(e2,x2,x4)).


    Outline81 l.jpg
    Outline semantic relations

    • Part III. Knowledge representation and inference

      • Representing the semantics of answers

      • Extended WordNet and abductive inference

      • Intentional Structure and Probabilistic Metonymy

      • An example of Event Structure

      • Modeling relations, uncertainty and dynamics

      • Inference methods and their mapping to answer types


    Intentional structure of questions l.jpg
    Intentional Structure of Questions semantic relations

    Example:Does have?

    x y

    Predicate-argument have/possess (Iraq, biological weapons)

    structureArg-0 Arg-1

    Question Pattern possess (x,y)

    Intentional Structure


    Coercion of pragmatic knowledge l.jpg
    Coercion of Pragmatic Knowledge semantic relations

    0*Evidence (1-possess (2-Iraq, 3-biological weapons)

    A form of logical metonymy


    The idea l.jpg
    The Idea semantic relations

    Logic metonymy is in part processed as verbal metonymy. We model, after Lapata and Lascarides, the interpretation of verbal metonymy as:

    where: v—the metonymic verb (enjoy)

    o—its object (the cigarette)

    e—the sought-after interpretation (smoking)


    A probabilistic model l.jpg
    A probabilistic model semantic relations

    By choosing the ordering , the probability may be factored as:

    where we make the estimations:

    This is a model of interpretation and coercion


    Coercions for intentional structures l.jpg
    Coercions for intentional structures semantic relations

    0*Evidence (1-possess (2-Iraq, 3-biological weaponry)


    Outline87 l.jpg
    Outline semantic relations

    • Part III. Knowledge representation and inference

      • Representing the semantics of answers

      • Extended WordNet and abductive inference

      • Intentional Structure and Probabilistic Metonymy

      • An example of Event Structure

      • Modeling relations, uncertainty and dynamics

      • Inference methods and their mapping to answer types


    Slide88 l.jpg

    ANSWER: Evidence-Combined: semantic relationsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reports add up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Slide89 l.jpg

    ANSWER: Evidence-Combined: semantic relationsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Slide90 l.jpg

    ANSWER: Evidence-Combined: semantic relationsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    Temporal Reference/Grounding

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Slide91 l.jpg

    Answer Structure (continued) semantic relations

    ANSWER: Evidence-Combined:Pointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Present Progressive Perfect

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    possess(Iraq, delivery systems(type : scud missiles; launchers; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium

    Present Progressive Continuing

    possess(Iraq, fuel stock(purpose: power launchers))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium

    hide(Iraq, Seeker: UN Inspectors; Hidden: CBW stockpiles & guided missiles)

    Justification: DETECTION SchemaInspection status: Past; Likelihood: Medium


    Slide92 l.jpg

    ANSWER: Evidence-Combined: semantic relationsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    Uncertainty and Belief

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Slide93 l.jpg

    ANSWER: Evidence-Combined: semantic relationsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    Uncertainty and Belief

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Mutliple Sources with reliability

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Slide94 l.jpg

    ANSWER: Evidence-Combined: semantic relationsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    Event Structure Metaphor

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium


    Event structure for semantically based qa l.jpg
    Event Structure for semantically based QA semantic relations

    • Reasoning about dynamics

      • Complex event structure

        • Multiple stages, interruptions, resources, framing

      • Evolving events

        • Conditional events, presuppositions.

      • Nested temporal and aspectual references

        • Past, future event references

      • Metaphoric references

        • Use of motion domain to describe complex events.

    • Reasoning with Uncertainty

      • Combining Evidence from Multiple, unreliable sources

      • Non-monotonic inference

        • Retracting previous assertions

        • Conditioning on partial evidence


    Relevant previous work l.jpg
    Relevant Previous Work semantic relations

    Event Structure

    Aspect (VDT, TimeML), Situation Calculus (Steedman), Frame Semantics (Fillmore), Cognitive Linguistics (Langacker, Talmy), Metaphor and Aspect (Narayanan)

    Reasoning about Uncertainty

    Bayes Nets (Pearl), Probabilistic Relational Models (Koller), Graphical Models (Jordan)

    Reasoning about Dynamics

    Dynamic Bayes Nets (Murphy), Distributed Systems (Alur, Meseguer), Control Theory (Ramadge and Wonham), Causality (Pearl)


    Outline97 l.jpg
    Outline semantic relations

    • Part III. Knowledge representation and inference

      • Representing the semantics of answers

      • Extended WordNet and abductive inference

      • Intentional Structure and Probabilistic Metonymy

      • An example of Event Structure

      • Modeling relations, uncertainty and dynamics

      • Inference methods and their mapping to answer types


    Structured probabilistic inference l.jpg
    Structured semantic relationsProbabilistic Inference


    Probabilistic inference l.jpg
    Probabilistic inference semantic relations

    • Filtering

      • P(X_t | o_1…t,X_1…t)

      • Update the state based on the observation sequence and state set

    • MAP Estimation

      • Argmaxh1…hnP(X_t | o_1…t, X_1…t)

      • Return the best assignment of values to the hypothesis variables given the observation and states

    • Smoothing

      • P(X_t-k | o_1…t, X_1…t)

      • modify assumptions about previous states, given observation sequence and state set

    • Projection/Prediction/Reachability

      • P(X_t+k | o_1..t, X_1..t)



    Outline101 l.jpg
    Outline semantic relations

    • Part IV. From Ontologies to Inference

      • From OWL to CPRM

      • FrameNet in OWL

      • FrameNet to CPRM mapping


    Semantic web l.jpg
    Semantic Web semantic relations

    • The World Wide Web (WWW) contains a large and expanding information base.

    • HTML is accessible to humans but does not formally describe data in a machine interpretable form.

    • XML remedies this by allowing for the use of tags to describe data (ex. disambiguating crawl)

    • Ontologies are useful to describe objects and their inter-relationships.

    • DAML+OIL (http://www.daml.org) is an markup language based on XML and RDF that is grounded in description logic and is designed to allow for ontology development, transfer, and use on the web.


    Slide103 l.jpg

    Programmatic Access to the web semantic relations

    Web-accessible programs and devices


    Knowledge rep n for the semantic web l.jpg

    OWL/ semantic relationsDAML-L (Logic)

    OWL (Ontology)

    RDFS (RDF Schema)

    RDF (Resource Description Framework)

    XML Schema

    Knowledge Rep’n for the “Semantic Web”

    XML (Extensible Markup Language)


    Knowledge rep n for semantic web services l.jpg

    DAML-L semantic relations(Logic)

    DAML+OIL (Ontology)

    RDFS (RDF Schema)

    RDF (Resource Description Framework)

    XML Schema

    Knowledge Rep’n for “Semantic Web Services”

    DAML-S (Services)

    XML (Extensible Markup Language)


    Daml s semantic markup for web services l.jpg
    DAML-S: Semantic Markup for Web Services semantic relations

    • DAML-S: A DARPA Agent Markup Language for Services

    • DAML+OIL ontology for Web services:

      • well-defined semantics

      • ontologies support reuse, mapping, succinct markup, ...

    • Developed by a coalition of researchers from Stanford, SRI,

    • CMU, BBN, and Nokia, Yale, under the auspices of DARPA.

    • DAML-S version 0.6 posted October,2001

      • http://www.daml.org/services/daml-s

    [DAML-S Coalition, 2001, 2002]

    [Narayanan & McIlraith 2003]


    Daml s owl s compositional primitives l.jpg

    inputs semantic relations

    (conditional) outputs

    preconditions

    (conditional) effects

    composedBy

    control

    constructs

    sequence

    while

    ...

    If-then-else

    fork

    DAML-S/OWL-S Compositional Primitives

    process

    atomic

    process

    composite

    process


    The owl s process description l.jpg
    The OWL-S Process Description semantic relations

    PROCESS.OWL


    Implementation l.jpg
    Implementation semantic relations

    DAML-S translation to the modeling environment KarmaSIM[Narayanan, 97] (http://www.icsi.berkeley.edu/~snarayan)

    Basic Program:

    Input: DAML-S description of Events

    Output: Network Description of Events in KarmaSIM

    Procedure:

    • Recursively construct a sub-network for each control construct. Bottom out at atomic event.

    • Construct a net for each atomic event

    • Return network


    Example of a wmd ontology in owl l.jpg
    Example of A WMD Ontology in OWL semantic relations

    <rdfs:Classrdf:ID="DevelopingWeaponOfMassDestruction">

    <rdfs:subClassOf rdf:resource= SUMO.owl#Making"/>

    <rdfs:comment>

    Making instances of WeaponOfMassDestruction.

    </rdfs:comment>

    </rdfs:Class>

    http://reliant.teknowledge.com/DAML/SUMO.owl


    Outline114 l.jpg
    Outline semantic relations

    • Part IV. From Ontologies to Inference

      • From OWL to CPRM

      • FrameNet in OWL

      • FrameNet to CPRM mapping


    The framenet project l.jpg
    The FrameNet Project semantic relations

    C Fillmore PI (ICSI)

    Co-PI’s: S Narayanan (ICSI, SRI)D Jurafsky (U Colorado) J M Gawron (San Diego State U)

    Staff:

    C Baker Project Manager

    B Cronin Programmer

    C Wooters Database Designer


    Frames and understanding l.jpg
    Frames and Understanding semantic relations

    • Hypothesis: People understand things by performing mental operations on what they already know. Such knowledge is describable in terms of information packets called frames.


    Framenet in the larger context l.jpg
    FrameNet in the Larger Context semantic relations

    • The long-term goal is to reason about the world in a way that humans understand and agree with.

    • Such a system requires a knowledge representation that includes the level of frames.

    • FrameNet can provide such knowledge for a number of domains.

    • FrameNet representations complement ontologies and lexicons.


    The core work of framenet l.jpg
    The core work of FrameNet semantic relations

    • characterize frames

    • find words that fit the frames

    • develop descriptive terminology

    • extract sample sentences

    • annotate selected examples

    • derive "valence" descriptions


    The core data l.jpg
    The Core Data semantic relations

    The basic data on which FrameNet descriptions are based take the form of a collection of annotated sentences, each coded for the combinatorial properties of one word in it. The annotation is done manually, but several steps are computer-assisted.


    Types of words frames l.jpg
    Types of Words / Frames semantic relations

    • events

    • artifacts, built objects

    • natural kinds, parts and aggregates

    • terrain features

    • institutions, belief systems, practices

    • space, time, location, motion

    • etc.


    Event frames l.jpg
    Event Frames semantic relations

    Event frames have temporal structure, and generally have constraints on what precedes them, what happens during them, and what state the world is in once the event has been completed.


    Sample event frame commercial transaction l.jpg
    Sample Event Frame: semantic relationsCommercial Transaction

    Initial state:Vendor has Goods, wants MoneyCustomer wants Goods, has Money

    Transition:Vendor transmits Goods to CustomerCustomer transmits Money to Vendor

    Final state:Vendor has Money

    Customer has Goods


    Sample event frame commercial transaction123 l.jpg
    Sample Event Frame: semantic relationsCommercial Transaction

    Initial state:Vendor has Goods, wants MoneyCustomer wants Goods, has Money

    Transition:Vendor transmits Goods to CustomerCustomer transmits Money to Vendor

    Final state:Vendor has Money

    Customer has Goods

    (It’s a bit more complicated than that.)


    Partial wordlist for commercial transactions l.jpg
    Partial Wordlist for Commercial Transactions semantic relations

    Verbs: pay, spend, cost, buy, sell, charge

    Nouns: cost, price, payment

    Adjectives: expensive, cheap


    Meaning and syntax l.jpg
    Meaning and Syntax semantic relations

    • The various verbs that evoke this frame introduce the elements of the frame in different ways.

      • The identities of the buyer, seller, goods and money

    • Information expressed in sentences containing these verbs occurs in different places in the sentence depending on the verb.


    Slide126 l.jpg

    She semantic relationsboughtsome carrotsfrom the greengrocerfor a dollar.

    Customer

    Vendor

    from

    BUY

    for

    Goods

    Money


    Slide127 l.jpg

    She semantic relationspaida dollarto the greengrocerfor some carrots.

    Customer

    Vendor

    to

    PAY

    for

    Goods

    Money


    Slide128 l.jpg

    She semantic relationspaidthe greengrocera dollarfor the carrots.

    Customer

    Vendor

    PAY

    for

    Goods

    Money


    Framenet product l.jpg
    FrameNet Product semantic relations

    • For every target word,

    • describe the frames or conceptual structures which underlie them,

    • and annotate example sentences that cover the ways in which information from the associated frames are expressed in these sentences.


    Complex frames l.jpg
    Complex Frames semantic relations

    • With Criminal_process we have, for example,

      • sub-frame relations (one frame is a component of a larger more abstract frame) and

      • temporal relations (one process precedes another)


    Framenet entities and relations l.jpg
    FrameNet Entities and Relations semantic relations

    • Frames

      • Background

      • Lexical

    • Frame Elements (Roles)

    • Binding Constraints

      • Identify

    • ISA(x:Frame, y:Frame)

    • SubframeOf (x:Frame, y:Frame)

    • Subframe Ordering

      • precedes

    • Annotation


    A daml oil frame class l.jpg
    A DAML+OIL Frame Class semantic relations

    <daml:Class rdf:ID="Frame">

    <rdfs:comment> The most general class </rdfs:comment>

    <daml:unionOf rdf:parseType="daml:collection">

    <daml:Class rdf:about="#BackgroundFrame"/>

    <daml:Class rdf:about="#LexicalFrame"/>

    </daml:unionOf>

    </daml:Class>

    <daml:ObjectProperty rdf:ID="Name">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="&rdf-schema;#Literal"/>

    </daml:ObjectProperty>


    Daml oil frame element l.jpg
    DAML+OIL Frame Element semantic relations

    <daml:ObjectProperty rdf:ID= "role">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="&daml;#Thing"/>

    </daml:ObjectProperty>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="frameElement">

    <daml:samePropertyAs rdf:resource="#role"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="FE">

    <daml:samePropertyAs rdf:resource="#role"/>

    </daml:ObjectProperty>


    Fe binding relation l.jpg
    FE Binding Relation semantic relations

    <daml:ObjectProperty rdf:ID="bindingRelation">

    <rdf:comment> See http://www.daml.org/services </rdf:comment>

    <rdfs:domain rdf:resource="#Role"/>

    <rdfs:range rdf:resource="#Role"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="identify">

    <rdfs:subPropertyOf rdf:resource="#bindingRelation"/>

    <rdfs:domain rdf:resource="#Role"/>

    <daml-s:sameValuesAs rdf:resource="#rdfs:range"/>

    </daml:ObjectProperty>


    Subframes and ordering l.jpg
    Subframes and Ordering semantic relations

    <daml:ObjectProperty rdf:ID="subFrameOf">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="#Frame"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="precedes">

    <rdfs:domain rdf:resource="#Frame"/>

    <rdfs:range rdf:resource="#Frame"/>

    </daml:ObjectProperty>


    The criminal process frame l.jpg
    The Criminal Process Frame semantic relations


    The criminal process frame in daml oil l.jpg
    The Criminal Process Frame in DAML+OIL semantic relations

    <daml:Class rdf:ID="CriminalProcess">

    <daml:subClassOf rdf:resource="#BackgroundFrame"/>

    </daml:Class>

    <daml:Class rdf:ID="CP">

    <daml:sameClassAs rdf:resource="#CriminalProcess"/>

    </daml:Class>


    Daml oil representation of the criminal process frame elements l.jpg
    DAML+OIL Representation of the Criminal Process Frame Elements

    <daml:ObjectProperty rdf:ID="court">

    <daml:subPropertyOf rdf:resource="#FE"/>

    <daml:domain rdf:resource="#CriminalProcess"/>

    <daml:range rdf:resource="&CYC;#Court-Judicial"/>

    </daml:ObjectProperty>

    <daml:ObjectProperty rdf:ID="defense">

    <daml:subPropertyOf rdf:resource="#FE"/>

    <daml:domain rdf:resource="#CriminalProcess"/>

    <daml:range rdf:resource="&SRI-IE;#Lawyer"/>

    </daml:ObjectProperty>


    Fe binding constraints l.jpg
    FE Binding Constraints Elements

    <daml:ObjectProperty rdf:ID="prosecutionConstraint">

    <daml:subPropertyOf rdf:resource="#identify"/>

    <daml:domain rdf:resource="#CP.prosecution"/>

    <daml-s:sameValuesAs rdf:resource="#Trial.prosecution"/>

    </daml:ObjectProperty>

    • The idenfication contraints can be between

      • Frames and Subframe FE’s.

      • Between Subframe FE’s

    • DAML does not support the dot notation for paths.


    Criminal process subframes l.jpg
    Criminal Process Subframes Elements

    <daml:Class rdf:ID="Arrest">

    <rdfs:comment> A subframe </rdfs:comment>

    <rdfs:subClassOf rdf:resource="#LexicalFrame"/>

    </daml:Class>

    <daml:Class rdf:ID="Arraignment">

    <rdfs:comment> A subframe </rdfs:comment>

    <rdfs:subClassOf rdf:resource="#LexicalFrame"/>

    </daml:Class>

    <daml:ObjectProperty rdf:ID="arraignSubFrame">

    <rdfs:subPropertyOf rdf:resource="#subFrameOf"/>

    <rdfs:domain rdf:resource="#CP"/>

    <rdfs:range rdf:resource="#Arraignment"/>

    </daml:ObjectProperty>


    Specifying subframe ordering l.jpg
    Specifying Subframe Ordering Elements

    <daml:Class rdf:about="#Arrest">

    <daml:subClassOf>

    <daml:Restriction>

    <daml:onPropertyrdf:resource="#precedes"/>

    <daml:hasClass rdf:resource="#Arraignment"/>

    </daml:Restriction>

    </daml:subClassOf>

    </daml:Class>


    Daml oil cp annotations l.jpg
    DAML+OIL CP Annotations Elements

    <fn:Annotation>

    <tpos> "36352897" </tpos>

    <frame rdf:about ="&fn;Arrest">

    <time> In July last year </time>

    <authorities> a German border guard </authorities>

    <target> apprehended </target>

    <suspect>

    two Irishmen with Kalashnikov assault rifles.

    </suspect>

    </frame>

    </fn:Annotation>


    Outline144 l.jpg
    Outline Elements

    • Part IV. From Ontologies to Inference

      • From OWL to CPRM

      • FrameNet in OWL

      • FrameNet to CPRM mapping


    Representing event frames l.jpg
    Representing Event Frames Elements

    • At the computational level, we use a structured event representation of event frames that formally specify

      • The frame

      • Frame Elements and filler types

      • Constraints and role bindings

      • Frame-to-Frame relations

        • Subcase

        • Subevent


    Events and actions l.jpg

    before Elements

    transition

    after

    actor

    undergoer

    nucleus

    Events and actions

    schema Event

    roles

    before : Phase

    transition : Phase

    after : Phase

    nucleus

    constraints

    transition :: nucleus

    schema Action

    evokes Event as e

    roles

    actor : Entity

    undergoer : Entity

    self« e.nucleus


    The commercial transaction schema l.jpg
    The Commercial-Transaction schema Elements

    schema Commercial-Transaction

    subcase of Exchange

    roles

    customer « participant1

    vendor « participant2

    money « entity1 : Money

    goods « entity2

    goods-transfer « transfer1

    money-transfer « transfer2


    Implementation148 l.jpg
    Implementation Elements

    DAML-S translation to the modeling environment KarmaSIM[Narayanan, 97] (http://www.icsi.berkeley.edu/~snarayan)

    Basic Program:

    Input: DAML-S description of Frame relations

    Output: Network Description of Frames in KarmaSIM

    Procedure:

    • Recursively construct a sub-network for each control construct. Bottom out at atomic frame.

    • Construct a net for each atomic frame

    • Return network


    Outline155 l.jpg
    Outline Elements

    • Part V. Results of Event Structure Inference for QA

      • AnswerBank

      • Current results for Inference Type

      • Current results for Answer Structure


    Answerbank l.jpg
    AnswerBank Elements

    • AnswerBank is a collection of over a 1200 QA annotations from the AQUAINT CNS corpus.

    • Questions and answers cover the different domains of the CNS data.

    • Questions and answers are POS tagged, and syntactically parsed.

    • Question and Answer predicates are annotated with PropBank arguments and FrameNet (when available) tags.

      • FrameNet is annotating CNS data with frame information for use by the AQUAINT QA community.

    • We are planning to add more semantic information including temporal, aspectual information (TIMEML+) and information about event relations and figurative uses.


    Slide157 l.jpg

    Retrieved Elements

    Documents

    Predicate Extraction

    C

    O

    N

    T

    E

    X

    T

    PRM

    <Pred(args), Topic Model, Answer Type>

    Model Parameterization

    FrameNet

    Frames

    <Simulation Triggering >

    Event Simulation

    OWL/OWL-S

    Topic

    Ontologies

    < PRM Update>




    Outline160 l.jpg
    Outline Elements

    • Part V. Results of Event Structure Inference for QA

      • AnswerBank

      • Current results for Inference Type

      • Current results for Answer Structure


    Answerbank data l.jpg
    AnswerBank Data Elements

    • We used 80 QA annotations from AnswerBank

      • Questions were of the four complex types

        • Justification, Ability, Prediction, Hypothetical

      • Answers were combined from multiple sentences (Average 4.3) and multiple annotations (average 2.1)

    • CNS Domains Covered were

      • WMD related (54%)

      • Nuclear Theft (25%)

      • India’s missile program (21%)


    Building models l.jpg
    Building Models Elements

    • Gold Standard:

      • From the hand-annotated data in the CNS corpus, we manually built CPRM domain models for inference.

    • Semantic Web based:

      • From FrameNet frames and from semantic web ontologies in OWL (SUMO-based, OpenCYC and others), we built CPRM models (semi-automatic)


    Event structure inferences l.jpg
    Event Structure Inferences Elements

    • For the annotations we classified complex event structure inferences as

      • Aspectual

        • Stages of events, viewpoints, temporal relations (such as start(ev1, ev2), interrupt(ev1, ev2))

      • Action-Based

        • Resources (produce,consume,lock), preconditions, maintenance conditions, effects.

      • Metaphoric

        • Event Structure Metaphor (ESM)

          Events and predications (motion => Action),

          objects (Motion.Mover => Action.Actor),

          Parameters(Motion.speed =>Action.rateOfProgress)


    Slide165 l.jpg

    ANSWER: Evidence-Combined: ElementsPointer to Text Source:

    A1: In recent months, Milton Leitenberg, an expert on biological weapons, has been looking

    at this murkiest and most dangerous corner of Saddam Hussein's armory.

    A2: He says a series of reportsadd up to indications that Iraq may be trying to develop a new

    viral agent, possibly in underground laboratories at a military complex near Baghdad where

    Iraqis first chased away inspectors six years ago.

    Answer Structure

    A3: A new assessment by the United Nations suggests Iraq still has chemical and

    biological weapons - as well as the rockets to deliver them to targets in other countries.

    A4:The UN document says Iraq may have hidden a number of Scud missiles, as well as

    launchers and stocks of fuel.

    A5: US intelligence believes Iraq still has stockpiles of chemical and biological weapons and

    guided missiles, which it hid from the UN inspectors

    Content: Biological Weapons Program:

    develop(Iraq, Viral_Agent(instance_of:new))

    Justification: POSSESSION Schema

    Previous (Intent and Ability): Prevent(ability, Inspection); Inspection terminated;

    Status: Attempt ongoing Likelihood: Medium Confirmability: difficult, obtuse, hidden

    possess(Iraq, Chemical and Biological Weapons)

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Prevent(ability, Inspection);

    Status: Hidden from Inspectors Likelihood: Medium

    possess(Iraq, delivery systems(type : rockets; target: other countries))

    Justification: POSSESSION SchemaPrevious (Intent and Ability): Hidden from Inspectors;

    Status: Ongoing Likelihood: Medium



    Conclusion l.jpg
    Conclusion Elements

    • Answering complex questions requires semantic representations at multiple levels.

      • NE and Extraction-based

      • Predicate Argument Structures

      • Frame, Topic and Domain Models

    • All these representations should be capable of supporting inference about relational structures, uncertain information, and dynamic context.

    • Both Semantic Extraction techniques and Structured Probabilistic KR and Inference methods have matured to the point that we understand the various algorithms and their properties.

    • Flexible architectures that

      • embody these KR and inference techniques and

      • make use of the expanding linguistic and ontological resources (such as on the Semantic Web)

    • Point the way to the future of semantically based QA systems!


    References url l.jpg
    References (URL) Elements

    • Semantic Resources

      • FrameNet: http://www.icsi.berkeley.edu/framenet (Papers on FrameNet and Computational Modeling efforts using FrameNet can be found here).

      • PropBank: http://www.cis.upenn.edu/~ace/

      • Gildea’s Verb Index; http://www.cs.rochester.edu/~gildea/Verbs/ (links FrameNet, PropBank, and VerbNet

    • Probabilistic KR (PRM)

      • http://robotics.stanford.edu/~koller/papers/lprm.ps (Learning PRM)

      • http://www.eecs.harvard.edu/~avi/Papers/thesis.ps.gz (Avi Pfeffer’s PRM Stanford thesis)

    • Dynamic Bayes Nets

      • http://www.ai.mit.edu/~murphyk/Thesis/thesis.pdf (Kevin Murphy’s Berkeley DBN thesis)

    • Event Structure in Language

      • http://www.icsi.berkeley.edu/~snarayan/thesis.pdf (Narayanan’s Berkeley PhD thesis on models of metaphor and aspect)

      • ftp://ftp.cis.upenn.edu/pub/steedman/temporality/temporality.ps.gz (Steedman’s article on Temporality with links to previous work on aspect)

      • http://www.icsi.berkeley.edu/NTL (publications on Cognitive Linguistics and computational models of cognitive linguistic phenomena can be found here)


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