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Applying Corpus Based Approaches using Syntactic Patterns and Predicate Argument Relations to Hypernym Recognition for Question Answering Kieran White and Richard Sutcliffe Contents Motivation Objectives Experimental Framework P-System Classifications Larger Evaluation

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slide1
Applying Corpus Based Approaches using Syntactic Patterns and Predicate Argument Relations to Hypernym Recognition for Question Answering

Kieran White and Richard Sutcliffe

contents
Contents
  • Motivation
  • Objectives
  • Experimental Framework
  • P-System Classifications
  • Larger Evaluation
  • Comparison of Three Models
  • Conclusions
motivation4
Motivation
  • Question answering and the DLT system
    • TREC, CLEF and NTCIR
  • Four stages to answering a factoid query in a standard question answering system
    • Identification of answer type required
    • Document retrieval
    • Named Entity recognition
    • Answer selection
slide5

Motivation

  • Example from TREC 2003
    • How long is a quarter in an NBA game?
    • Identity type of answer required
      • length_of_time
    • Document retrieval
      • Boolean query: quarter AND NBA AND game
      • Relax query if no documents returned
slide6

Motivation

  • Example from TREC 2003
    • Named Entity recognition
      • Locate instances of length_of_time Named Entities in documents
    • Answer selection
      • Select one length_of_time Named Entity (e.g. 12 minutes) using a scoring function
slide7

Motivation

  • Comparing query terms with those in supporting sentences
    • White and Sutcliffe (2004)‏
    • Compared terms in
      • 50 TREC question answering factoid queries from 2003
      • Supporting sentences
    • Morphological relationships
      • Identical terms (e.g. Washington Monument and Washington Monument)‏
      • Different inflections of terms (e.g. New York and New York's
      • Terms with different parts of speech (e.g. France and French)‏
slide8

Motivation

  • Comparing query terms with those in supporting sentences
    • Semantic relationships
      • Synonyms (e.g. orca and killer whale)‏
      • Terms linked by a causal relationship (e.g. die and typhus)‏
      • Word chains (e.g. Oscar and best-actress)‏
      • Hypernyms (e.g. city and Berlin)‏
      • Hyponyms (e.g. Titanic and ship)‏
      • Meronyms (e.g. Death Valley and California Desert)‏
      • Holonyms (e.g. 20th century and 1945)‏
      • Attributes and units to quantify them (e.g. hot and degrees)‏
      • Co-occurrence (e.g. old and 18)‏
slide9

Motivation

  • Comparing query terms with those in supporting sentences
    • Hypernyms / Hyponyms most common semantic relationship type
  • TREC Examples
    • What stadium was the first televised MLB game played in?
      • In 1939, the first televised major league baseball games were shown on experimental station W2XBS when the Cincinnati Reds and the Brooklyn Dodgers split a doubleheader at Ebbets Field.
slide10

Motivation

  • TREC Examples
    • What actress has received the most Oscar nominations?
      • Oscar perennial Meryl Streep is up for best actress for the film, tying Katharine Hepburn for most acting nominations with 12.
    • What ancient tribe of Mexico left behind huge stone heads standing 6-11 feet tall?
      • The company's founder, geophysicist Sheldon Breiner, is a Stanford University graduate who used the first cesium magnetometer to discover two colossal and ancient Olmec heads in Mexico in the 1960s.
slide11

Motivation

  • TREC Examples
    • When was the Titanic built?
      • The techniques used today to analyze the defects in the metal did not exist back in 1910 when the ship was being built, he said.
slide12

Motivation

  • How to classify?
    • Common nouns in ontologies (e.g. WordNet)‏
    • Proper nouns???
  • Feature co-occurrence
    • Labelling clusters of semantically related terms (Pantel and Ravichandran, 2004)‏
    • Responding with the hypernym of similar previously classified hyponyms (Alfonseca and Manandhar, 2002; Pekar and Staab, 2003; Takenobu et al., 1997)‏
slide13

Motivation

  • Search patterns (Fleischman et al., 2003; Girju, 2001; Hearst, 1992; 1998; Mann, 2002; Moldovan et al., 2000)‏
  • Feature co-occurrence and search patterns (Hahn and Schattinger,1998)‏
objectives15
Objectives
  • Create one or more hyponym classifiers for use as a component in a question answering system
  • Evaluate accuracy of classifiers when identifying the occupations of people
experimental framework17
Experimental Framework
  • P-System
    • Takes a name as input and attempts to respond with the person's occupation
    • Predicate-argument co-occurrence frequencies
  • A-System
    • Takes a name as input and attempts to respond with the person's occupation
    • Search pattern
slide18

Experimental Framework

  • H-System
    • Takes a name as input and attempts to respond with the correct occupation sense
    • P-System and A-System hybrid
slide19

Experimental Framework

  • AQUAINT corpus (Graff, 2002)‏
  • List of 364 occupations
  • 257 names from 2002-2004 TREC Question Answering track queries
    • 250 were classifiable by a person
p system classifications21
P-System Classifications
  • Minipar (Lin, 1998)‏
  • Subject‑verb and verb‑object pairs
  • Frequencies passed to Okapi's BM25 matching function
    • Candidate hypernyms (occupations) indexed as documents
    • Hyponyms (names) presented as queries
  • Ordered list of hypernyms returned in response to a hyponym
  • Top-ranking hypernym selected as answer
slide22

P-System Classifications

  • 50 names from TREC queries
  • No co-reference resolution
    • 0.30 accuracy
  • Full names substituted for partial names
    • 0.44 accuracy
slide23

P-System Classifications

  • Accuracy increases with name co-occurrence frequency
  • Occupation co-occurrence frequency was not a limiting factor in our experiments
  • Grouping similar occupations allowed us to perform a 194-way classification experiment
    • Accuracy increased from 0.44 to 0.56
slide24

P-System Classifications

  • Tuning constants provide some control over the specificity of occupations returned
    • Best assignment of constants penalises occupations occurring in fewer than 1,000 predicate-argument pairs
  • Some occupations could be classified better than others
    • Which could P-System classify accurately?
    • How accurately?
    • Test set too small
larger evaluation26
Larger Evaluation
  • Apposition pattern
    • Provides reference judgements
  • Ontology of occupations and an ontological similarity measure
    • Quantifies similarity between returned occupation and nearest occupation in reference judgements
  • Threshold for the ontological similarity measure
    • A value greater than or equal to this indicates that the response of P-System is correct
slide27

Larger Evaluation

  • Apposition pattern
    • Search pattern

occupation,? Capitalised Word Sequence

    • Examples
      • For the past year, actorAaron Eckhart has been receiving hate mail.
      • The landlord, Jon Mendelson, said he would consider any offer from Simmons.
slide28

Larger Evaluation

  • Apposition pattern
    • 107,958 distinct capitalised word sequences found in apposition with an occupation
    • In a random sample of 1,000 instances
      • 801 were correct
      • 93 attributed some role to a person rather than their occupation (e.g. referred to the leader of an organisation as a chief)‏
      • 56 indicated an incorrect occupation
      • 50 capitalised word sequence did not refer to the complete name of a person
slide29

Larger Evaluation

  • Ontology of occupations
    • Manually constructed ontology of hypernyms
    • Internal nodes comprise filler nodes that provide structure and occupations from the list of 364
    • Leaf nodes are all taken from the list of occupations
slide30

Larger Evaluation

  • Extract from ontology
slide31

Larger Evaluation

  • Similarity Measure
    • Semantic Association Measure (SAM) between two nodes is calculated by
      • Assigning a weight, w, to each edge where if c1 represents the number of successors of a node in the ontology and c2 is the number of successors of one of its children then
      • Summing the weights of all edges between the two nodes to determine the distance, d
      • Finally,
slide32

Larger Evaluation

  • Calculating SAM
slide33

Larger Evaluation

  • Similarity Threshold
    • Identified the best from a range of candidate thresholds between 0.20 and 0.40
    • Compared a manual evaluation of P-System over 200 names in apposition with an occupation...
    • ...to automated evaluation method using a candidate threshold to produce a binary judgements
slide34

Larger Evaluation

  • Similarity Threshold
    • If the SAM between the occupation returned by P-System and the nearest occupation in apposition was...

>= candidate threshold

      • Right by automated evaluation

< candidate threshold

      • Wrong by automated evaluation
slide35

Larger Evaluation

  • Similarity Threshold
    • Calculated proportion of times the automatic and manual evaluations agreed in their judgements of a response
    • Selected candidate threshold with largest agreement level
      • Threshold 0.28
      • Agreement level of 0.872
      • Or 0.848 where unusual but otherwise correct classifications was also considered right.
      • High agreement levels validate evaluation method
slide36

Larger Evaluation

  • P-System was tested on the 3,177 names
  • That exists in apposition with at least one occupation
  • Which are present in at least 100 predicate-argument pairs
  • Responses were automatically evaluated
slide37

Larger Evaluation

  • Classification accuracy for actors was 0.955
  • 1.00 > accuracy >= 0.75
    • Actor, author, quarterback, prosecutor, singer, boxer, premier, coach, attorney, lawyer, politician
  • 0.75 > accuracy>= 0.50
    • Spokesman, minister, senator, governor, president, baseman, fielder
slide38

Larger Evaluation

  • 0.50 > accuracy >= 0.25
    • Writer, runner, expert, killer, guard, executive, leader, hero, brother, player, captain
  • 0.25 > accuracy >= 0.00
    • General, officer, driver, chief, veteran, chairman, director, manager, agent, host
comparison of three models40
Comparison of Three Models
  • A-System
    • Uses apposition instances from previous experiment
    • Returns occupation that occurs most frequently in apposition with input name
slide41

Comparison of Three Models

  • H-System
    • P-System and A-System hybrid
    • If P and A both return an occupation
      • Returns the occupation sense that occurs in apposition that is closest to the response of P
    • If only P returns an occupation
      • Returns a sense of the response of P
    • If only A returns an occupation
      • Returns a sense of the response of A
slide42

Comparison of Three Models

  • Three-way comparison between P, A and H
  • 250 classifiable TREC names
  • Manual evaluation
  • Compared the three models
    • In both a strict and lenient evaluation
    • Where all names were classified and also where just those names occurring in at least 100 predicate-argument pairs were classified
    • Controlled for the ability of A to classify a name
slide43

Comparison of Three Models

  • H is most accurate across all names
    • Significantly better in lenient evaluation
      • Accuracies: H 0.584, A 0.492, P 0.424
    • In strict evaluation H is also the most accurate
  • A only attempted to classify 0.632 of names
    • H and P attempted 0.904 and 0.892 of names
slide44

Comparison of Three Models

  • On names that were found in apposition with an occupation
    • In the lenient evaluation H was most accurate
      • Accuracies: H 0.797, A 0.778, P 0.544
    • In the strict evaluation A was the best
      • Accuracies: H 0.722, A 0.728, P 0.462
slide45

Comparison of Three Models

  • H-System returns more general occupations than A-System
    • An advantage for it in the lenient evaluation
    • A disadvantage in the strict evaluation
  • The principle of combining two very different approaches to classification has been validated
conclusions47
Conclusions
  • Combining two classification models such as in H-System allowed us to
    • Respond with high accuracy
    • Increase Recall beyond that of component classifiers
  • Experiments demonstrate that we can
    • Identify hypernyms of proper nouns such as people's names
    • In the context of question answering