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Prepositional Phrase Attachment Problem. 03M05601 Ashish Almeida. Overview. Introduction to NLP Analysis in UNL system Prepositional phrase attachment problem Proposed method to handle this problem. Motivation. Analysis involves many complex problems

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Prepositional Phrase Attachment Problem

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Prepositional phrase attachment problem l.jpg

Prepositional Phrase Attachment Problem

03M05601

Ashish Almeida

PP attachment problem


Overview l.jpg

Overview

  • Introduction to NLP

  • Analysis in UNL system

  • Prepositional phrase attachment problem

  • Proposed method to handle this problem

PP attachment problem


Motivation l.jpg

Motivation

  • Analysis involves many complex problems

  • Prepositional phrase attachment problem is one such difficult problem.

  • If solved, improve the quality of information extracted manifold

  • No existing system solves the problem

PP attachment problem


Tasks involved in nlp l.jpg

NL understanding

Text

Meaning

NL generation

Tasks involved in NLP

Analysis and generation

PP attachment problem


Phases in nlp l.jpg

Phases in NLP

  • Morphological analysis

  • Syntactic analysis

  • Semantic analysis

  • Discourse integration

  • Pragmatic analysis

PP attachment problem


Is nl compositional l.jpg

Is NL Compositional ?

  • Compsitional expression

    • Meaning of the whole from meaning of parts

      e.g.strong tea

      -rich tea

      day by day

      - all the time

PP attachment problem


Analysis l.jpg

Analysis

Morphological + Syntactic + Semantic analysis

  • All these phases are dependent on each other.

  • Interactive Vs modular approach

  • Analysis in UNL system - interactive

PP attachment problem


Slide8 l.jpg

UNL …

  • UNL is Interlingua

    e.g. Ram ate rice with spoon.

eat(icl>do)

@ entry

@ present

agt

ins

obj

John(iof>person)

spoon(icl>artifact)

rice(icl>food)

PP attachment problem


Unl expresion l.jpg

UNL expresion

UNL Expression for Ram ate rice with spoon.

agt(eat(icl>do).@past.@entry, Ram(iof>person))

obj(eat(icl>do).@past.@entry, rice(icl>food))

ins(eat(icl>do).@past.@entry, spoon(icl>tool))

agt(eat(icl>do).@past.@entry, Ram(iof>person))

Relation

UWs

Attributes

PP attachment problem


Analysis in unl l.jpg

Analysis in UNL

  • Enconverter

    • Natural Language to UNL

    • Handles one sentence at a time

    • Predicate preserving parser

    • Kind of Turing machine

  • Components

    • Dictionary : lexical units, uw, semantic attributes

    • Rule base : head movement rules, relation resolving rules

  • Working

    • Uses dictionary and rule bases to process the sentence.

PP attachment problem


Prepositional phrase attachment problem11 l.jpg

Prepositional Phrase Attachment Problem

  • Type of Structural ambiguity in a sentence

Verb attachment

JohnNP

readVP

the reportNP

on new technologies.PP

Noun attachment

PP attachment problem


Prepositional phrase attachment problem12 l.jpg

read

read

John

the report

John

on

the report

on

new technologies

new technologies

Prepositional Phrase Attachment Problem…

  • Noun attachment Vs verb attachment

    e.g. John read the report on new technologies.

*

PP attachment problem


Establishing semantic relation l.jpg

Establishing semantic relation

Same structure-different semantic relation

e.g.

1. Ram ate rice withspoon.……instrument

The UNL for this sentence is

ins(eat(icl>do).@past.@entry, spoon(icl>tool))

2. Ram ate rice with Sita.……co-agent

The UNL for this sentence is

cag(eat(icl>do).@past.@entry, Sita(iof>person))

PP attachment problem


Difficult problem l.jpg

Difficult problem

  • PP attachment problem is simpler or no problem for human being

    - who use world knowledge to process it.

  • This world knowledge is not available to machines.

    e.g. travel by night …time

    travel by bus …instrument

PP attachment problem


Different sites of attachment l.jpg

Different sites of attachment

  • The searchforthe policy is going on.

  • The test will be held at the endofAugust.

  • InAugust 1947, India became free from British rule.

  • Wilson received a medal from the commanding officerata farewell party.

  • There is no restriction on how far the PP can lie from the word to which it relates.

  • PP attachment problem


    Affinity with preceding phrase l.jpg

    Affinity with preceding phrase

    • The preposition ofgets attached to a noun phrase or a verb phrase immediately preceding it.

      • They were involved in the murder of a 90-year-old woman.

      • It was begun last week by the crew of a giant crane-barge.

      • He died of an overdose of sleeping pills

      • The system will be tailored to meet the need of the political party.

    PP attachment problem


    Existing methods l.jpg

    Existing methods

    • generate mod-obj combination for almost all PP relations

      • E.g He came according to his promise.

        agt(come(icl>do)@past.@entry, he)

        *mod(come(icl>do)@past.@entry, :01)

        obj:01(according to, promise(icl>abstract thing))

        mod:01(promise(icl>abstract thing),he)

    • Tags introduced manually to resolve phrase boundaries

      • E.g. It delineates <p>the scope of phrases</p> before <p>conversion of the sentence</p>.

    PP attachment problem


    Related work l.jpg

    Related work

    • Statistical learning methods used

    • Wordnet is used to find relations between words

    • Analysis of corpus is required

    • Not all aspects of problem considered

    • The hypothesis does not apply to all cases

      “PP attachments obey the principle of locality”

    PP attachment problem


    Observations l.jpg

    Frequency

    Preposition

    Poly. count

    29391

    of

    7

    18214

    in

    10

    9343

    to

    8

    14

    by way of

    1

    16

    by means of

    1

    Observations

    • Prepositions frequency is calculated from British National Corpus

    • Classified into 2 parts

      • Simple Preposition

      • Ambiguous prepositions

    PP attachment problem


    Addition to semantic attributes hierarchy l.jpg

    Addition to Semantic Attributes hierarchy

    • Semantic attributes required to disambiguate

    • Addition required, if existing attributes fail to classify

    • necessary condition

      • the attributes should be able to classify the semantically separate structures as separate entities.

        e.g.the train for Delhi….to()

        the price for the Hill Road pool….mod()

    PP attachment problem


    Inclusion of preposition in unl expression l.jpg

    Inclusion of preposition in UNL expression

    • a picture on the wall

      plc(picture, wall).

    • The cat walked across the street.

      • Wrong UNL

        *plc ( walk, street )

        -cat walked along the street

        -cat walked across the street

      • Correct UNL

        plc (walk, :01)

        obj:01(across, street)

    PP attachment problem


    Classification based on syntax structure l.jpg

    [ Verb + for + Noun phrase]

    v-pur

    He was waiting for the rainy day.

    v-pur

    He applied for a certificate.

    [ Noun phrase + for + Noun phrase]

    n-mod

    The search for the policy is going on.

    n-mod

    He pays the price for his indulgence.

    Classification based on syntax structure

    • Sentences have different syntactic structure

    • Parsing the depends on surface structure

      - Active-passive, transitive-di-transitive, present-past participles etc.

    • Classification based on syntax pattern

    PP attachment problem


    Classification based on semantics l.jpg

    Relation

    Example sentence

    ON

    plc

    a picture on a wall

    ins

    to travel on the bus

    tim

    He came on Sunday

    seq

    Report to reception on arrival

    mod

    a book on South Africa

    ins

    She played a tune on her guitar

    plc

    You can get me on 0181 530 3906

    Classification based on semantics

    • Deciding factors

      • Syntax, attributes, preposition, subcategorisation frame(for verbs)

        Partial list of preposition on and its possible semantic relation

    PP attachment problem


    Updating rule base l.jpg

    Comment

    ;N/abs for N/abs ;search for policy

    delete preposition for

    DL(N,ABS)

    {PRE,#FOR:::}

    {N,ABS:+PRERES,+FORRES,+pPUR::}P25;

    Comment

    ;V FOR N-UNIT-QUARES ;suspend for 2 days

    Delete preposition for

    DL(VRB){PRE,#FOR:::}

    {N,UNIT,TIM,QUARES

    :+PRERES,+FORRES,+pDUR::}P30;

    Updating rule base

    • Simpler if the classification is perfect.

    • Issues involved

      • Priority, proper specification

        Two rules showing difference in priority – specific to general

    PP attachment problem


    Conclusion l.jpg

    Conclusion

    • World knowledge is realized in terms of semantic attributes.

    • Phrasal verbs are not considered

    • Idiomatic constructs are not handled

      - e.g. day by day

      all the time

    PP attachment problem


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