Word sense disambiguation and information retrieval

1
Word sense disambiguation and information
retrieval

• Chapter 17
• Jurafsky, D. Martin J. H.
• SPEECH and LANGUAGE PROCESSING
• Jarmo Ritola - jarmo.ritola_at_hut.fi

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Lexical Semantic Processing

• Word sense disambiguation
• which sense of a word is being used
• non-trivial task
• robust algorithms
• Information retrieval
• broad field
• storage and retrieval of requested text documents
• vector space model

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Word Sense Disambiguation

• (17.1) ..., everybody has a career and none of
  them includes washing DISHES
• (17.2) In her tiny kitchen at home, Ms. Chen
  works efficiently, stir-frying several simple
  DISHES, including braised pigs ears and chicken
  livers with green peppers
• (17.6) Im looking for a restaurant that SERVES
  vegetarian DISHES

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Selectional Restriction

• Rule-to-rule approach
• Blocks the formation of representations with
  selectional restriction violations
• Correct sense achieved as side effect
• PATIENT roles, mutual exclution
• dishes stir-fry gt food sense
• dishes wash gt artifact sense
• Need hierarchical types and restrictions

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S.R. Limitations

• Selectional restrictions too general
• (17.7) kind of DISHES do you recommend?
• True restriction violations
• (17.8) you cant EAT gold for lunch
• negative environment
• (17.9) Mr. Kulkarni ATE glass
• Metaphoric and metonymic uses
• Selectional association (Resnik)

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Robust Word Sense Disambiguation

• Robust, stand alone systems
• Preprocessing
• part-of-speech tagging, context selection,
  stemming, morphological processing, parsing
• Feature selection, feature vector
• Train classifier to assign words to senses
• Supervised, bootstrapping, unsupervised
• Does the system scale?

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Inputs Feature Vectors

• Target word, context
• Select relevant linguistic features
• Encode them in a usable form
• Numeric or nominal values
• Collocational features
• Co-occurrence features

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Inputs Feature Vectors (2)

• (17.11) An electric guitar and BASS player stand
  off to one side, not really part of the scene,
  just as a sort of nod to gringo expectations
  perhaps.
• Collocational
• guitar, NN1, and, CJC, player, NN1, stand,
  VVB
• Co-occurrence
• 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0
• fishing, big, sound, player, fly, rod, pound,
  double, runs, playing, guitar, band

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Supervised Learning

• Feature-encoded inputs categories
• Naïve Bayes classifier
• Decision list classifiers
• case statements
• tests ordered according to sense likelihood

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Bootstrapping Approaches

• Seeds, small number of labeled instances
• Initial classifier extracts larger training set
• Repeat gt series of classifier with improving
  accuracy and coverage
• Hand labeling examples
• One sense per collocation
• Also automatic selection from machine readable
  dictionary

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Unsupervised Methods

• Unlabeled feature vectors are grouped into
  clusters according to a similarity metric
• Clusters are labeled by hand
• Agglomerative clustering
• Challenges
• correct senses may not be known
• heterogeneous clusters
• Number clusters and senses differ

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Dictionary Based Approaches

• Large-scale disambiguation possible
• Sense definitions retrieved from the dictionary
• The sense with highest overlap within context
  words
• Dictionary entries relative short
• Not enough overlap
• expand word list, subject codes

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Information Retrieval

• Compositional semantics
• Bag of words methods
• Terminology
• document
• collection
• term
• query
• Ad hoc retrieval

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The Vector Space Model

• List of terms within the collection
• document vector presence/absence of terms
• raw term frequency
• normalization gt direction of vector
• similarity is cosine of angle between vectors

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The Vector Space Model

• Document collection
• Term by weight matrix

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Term Weighting

• Enormous impact on the effectiveness
• Term frequency within a single document
• Distribution of term across a collection
• Same weighting scheme for documents and queries
• Alternative weighting methods for queries
• AltaVista di,j contains 1000000000 words
• average query 2.3 words

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Recall versus precision

• Stemming
• Stop list
• Homonymy, polysemy, synonymy, hyponymy
• Improving user queries
• relevance feedback
• query expansion, thesaurus, thesaurus generation,
  term clustering

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Summary

• WSD assign word to senses
• Selectional restriction
• Machine learning approaches (small scale)
• supervised, bootstrapping, unsupervised
• Machine readable dictionaries (large scale)
• Bag of words method, Vector space model
• Query improvement (relevance feedback)

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Exercise - Relevance Feedback
The document collection is ordered according to
the 'raw term frequency' of words "speech" and
"language". The values and ordering is shown in
the table below.

• You want to find documents with many "speech"
  words but few "language" words (e.g. relation 8
  2). Your initial query is "speech", "language",
  i.e. they have equal weights.
• The search machine always returns three most
  similar documents.
• Show that with relevance feedback you
• get the documents you want.
• How important is the correctness of
• feedback from the user?