Improving Subcategorization Acquisition using Word Sense Disambiguation

1
Improving Subcategorization Acquisition using
Word Sense Disambiguation

• Anna Korhonen and Judith Preiss
• University of Cambridge, Computer
  Laboratory
• 15 JJ Thomas Avenue, Cambridge CB3
  0FD, UK
• Anna.Korhonen_at_cl.cam.ac.uk,
  Judita.Preiss_at_cl.cam.ac.uk

2
Outline

• Subcategorization Acquisition
• Baseline System
• Baseline System combined with WSD
• Probabilistic WSD
• Experiment
• Evaluation
• Methods

3
Introduction

• Subcategorization
• The dependents of a verb are classified in
• arguments -subject, object, direct
  object
• - subject
• - non subject
  arguments (complements)
• e.g. Mary knows that she is wining.
• adjuncts
• e.g. She read the book with great
  interest.
• The type of complements that a verb permits
  gives the verb classification
• The verb classification is called
  subcategorization
• SCFs subcategorization frames for a
  given
• predicate essential for parsing

4
Introduction

• SCFs- a particular set of arguments that a verb
  can appear with
• Intransitive verb. NPsubject. They danced.
• Transitive verb. NPsubject, NPobject. Mary
  appreciates
  her Professor.
• Intransitive with PP. NPsubject,PP. He
  leave in Paris Transitive with PP.
  NPsubject, NPobject, PP. She put the book on
  the table.

5
Introduction

• Manual subcategorization versus automatically one
• Manual - does not provide the relative frequency
  of SCFs
• - predicates change behavior
• Automatically - no lexical/semantic
  information is exploited
• - reveals only syntactic aspects
• - no distinction between predicate
  senses
• Korhonen(2002) model back-off estimates
  which used the predominant sense of a verb
  (WordNet)
• Acquisition Goal domain specific lexicon
  (written vs. spoken genre based on
  different senses)

6
Subcategorization Acquisition

• Baseline System
• system with the knowledge of verb semantics
• Levin(93)
• - verb senses divides them in classes
  distinctive for subcategorization
• Korhonen(2002)
• - verb forms are able to divide them into
  semantic classes based on the
  predominant sense (fly - move)
• - determine the sense and the
  semantic class (Levin Classes Motion
  verbs)
• Briscoe Carroll(97) SCF distribution
  are acquired from corpus data

7
Subcategorization Acquisition

• Baseline System description
• The linear interpolation smoothing back-off
  estimates is used for the SCF distribution
• The method of obtaining back-off estimates
• a) 4-5 representative verbs are chosen
  from a verb class
• b) for theses verbs the SCF distribution is
  built using manually analysis of 300
  occurrences of each verb (BNC)
• c) the resulted SCF distributions are
  merged giving equal weight to each
  distribution
• E.g. fly - move, slide, arrive,
  travel, sail
• An empirical threshold is used to filter out
  noisy SCFs

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Subcategorization Acquisition

• Combining with WSD
• Preiss Korhonen(02)
• - created different corpus datasets for the
  senses (first/and or second) being
  disambiguated and other datasets for the
  remaining senses
• - SCFs were acquired from both types of
  datasets
• - back-off estimates used for the SCFs acquired
  from the initial dataset, the estimates
  were used for smoothing according to the
  relevant sense
• - the SCF lexicons acquired were merged in the
  end SCF distribution was rather specific to a
  verb than a sense
• - problems with subcategorization acquisition
  datasets too small, separation of the data
  was unnecessary

9
Subcategorization Acquisition

• New method
• does not involve separating data and
  it uses back-off estimates for the sense
  distribution given by the WSD system not only
  for the predominant sense
• pj(scfi), j1..nb0 (nb0the number of
  back-off estimates)
• - the probabilities of SCFs in
  different back-off distribution
• P(scfi) ??jpj(scfi)
• ?j - weights for the different distributions
  that sum up to 1,
  are obtained from the probabilistic WSD system
  
• Probabilistic WSD
• - able to determine the probability
  distribution for each noun, verb, adjective
  and adverb
• - able to determine a probability distribution
  on the senses for each verb and compute the
  average of it

nb0
J1
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Subcategorization Acquisition

• System Description
• - it is based on Stevenson and Wilks(2001)
  system which combines knowledge sources to
  produce a WSD Tool
• - it combines the probability distribution on
  senses determined by each module used
  (modules described in Yarowsky(2000)
  Mihalcea(2002) Pederson(2002)) for the WSD
  probabilistic system
• - a process of smoothing is used for each
  module according to each confidence value a low
  module confidence is smoothed extensively for
  uniform distribution
• - the optimal combination of modules is based
  on the accuracy (F-measure) for the English
  all-words task

11
Subcategorization Acquisition

• Experiment
• Test Data
• - polysemous verbs with the predominant
  sense not very frequent 29 verbs
  chosen randomly
• - the Levin-style senses are used to map the
  WordNet senses of the chosen verbs
• - he maximum number of Levin
  senses considered was 4 and some of the
  given senses were left out

12
Subcategorization Acquisition
13
Subcategorization Acquisition

• Evaluation
• Method
• - 20 mil words of the BNC corpus and
  extracted all senses for the test verbs
• - 1000 sentences for each verb
  disambiguated with the probabilistic
  WSD
• - applied the modified subcategorization
  system
• - for each verb an individual set of
  back-off estimates was built based on
  the different frequency senses from the
  corpus data
• - results were evaluated against a manual
  analysis of the corpus data
• - for an average of 300 occurrences for
  each verb in the BNC test data 5-21
  gold standard SCFs were found (16 SCFs
  per verb)

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Subcategorization Acquisition

• Evaluation
• Method
• F-measure 2PR / PR
• P-precision
• R-recall
• RC Sperman rank correction
• KL Kullback-Leibler distance
• CE cross entropy
• - record the total number of SCFs
  missing in the distribution for
  determining the accuracy of the back-off
  estimates
• - comparison with other systems the
  base-line and other which assumed no sense
  at all

15
Subcategorization Acquisition

• Results
• - using the unsmoothed lexicon from a total of
  175 unseen standard SCFs a number of 107 remain
  unseen after using the predominant sense method
• - using the WSD method only 22 remain unseen
• the performance improves with the numbers of
  senses
• - IS measure reveals that between the acquired
  and the gold standard SCFs exists an
  intersection when WSD is used

16
Subcategorization Acquisition
17
Subcategorization Acquisition

• Results
• - improvement for the highly polysemous verbs
  (bear, count, roar e.t.c)
• - verbs who differ substantially in terms of
  subcategorization (conceive, continue, grasp
  e.t.c)
• - verbs whose sense involves mainly NP/PP
• - SCFs seems to appear in data as families
  for a sense of a verb
• - worse performance for seek using WSD even
  though is highly polysemous and differs in
  terms of subcategorization
• -no clear improvement choose, compose,
  induce, watch

18
Subcategorization Acquisition

• Conclusions
• - using the WSD an improvement can be shown
  for SCFs acquisition of difficult verbs because
  the senses differ also in terms of
  subcategorization not only in the degree of
  polysemy
• Future work
• - a better way of integrating the frequency of
  acquired senses into the SCFs and a refinancefor
  the subcategorization method