Word Association Thesaurus as a Resource for Building Wordnet

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Word Association Thesaurus as a Resource for
Building Wordnet

• Anna Sinopalnikova
• Masaryk University, Brno, Czech Republic
• Saint-Petersburg State University, Russia
• anna_at_fi.muni.cz

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Overview

• Types of LRs used
• What is Word Association?
• Information to be extracted from WAT
• WAT vs. Corpus
• Conclusions
• Future plans

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What kind of language resources are used to build
wordnets?

• Primary resources
• e.g. text corpora
• present (more or less) raw data on the language
  in use
• information is given implicitly

• Derived resources
• e.g. explanatory dictionaries, Roget type
  thesauri
• present explications of internal knowledge of
  language
• based on primary resources intuition
• information is given explicitly

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What is better?

• To build an adequate and reliable lexical
  database (e.g. wordnet) it is not enough to rely
  upon information produced by experts (i. e.
  linguists, lexicographers).
• One should rather explore the raw data, and
  extract information from language in its actual
  and its potential use.
• Corpora reign!

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Word Association

• Association connection or relation between 2
  entities (perceptions, ideas or words), that
  manifests in a following way an appearance of
  one entity entails the appearance of the other in
  the mind
• Word Association an appearance of one word
  entails the appearance of the other in the mind

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Association examples (1)

• Kill ?

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Association examples (1)

• Kill ? ? Bill

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Association examples (2)
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Association examples (2)

• ? Nike

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Association examples (3)

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Association examples (3)

• 
  ? Kill Bill

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Word Association Test

• Generally, a list of words (stimuli) is given to
  subjects (either in writing or in oral form). The
  subjects are asked to respond with the first word
  that comes into their mind (responses).
• Other methods controlled association test,
  priming etc.

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Cat stimulates

• Dog 49, mouse 8, black 4, animal 2, eyes, gut,
  kitten, tom 2, bit, Cheshire, claw, claws,
  enigma, feline, furry, hearth, house, kin,
  kittens, milk, pet, pussy, todd 1
• (of 100 people asked)

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Word Association Norms (WAN)

• WAN represents the data collected through a
  series of WA test carried out according to the
  standard technique.
• The body of WAN list of responses and their
  absolute frequencies for each stimulus word
• E.g. Kent Rosanoff (1910) 100 stimuli
  - 1000 subjects
• Palermo Jenkins (1964) 200 stimuli - 1000
  subjects

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Word Association Thesaurus (WAT)

• WAT is a kind of WAN
• WAN vs. WAT differ not only in volume but also in
  the procedure of data collection. It implies
  cycles A small set of stimuli is used as a
  starting point of the experiment, responses
  obtained for them are used as stimuli in the next
  stage, the cycle being repeated at least 3 times.
  
• Being a thesaurus WAT is expected to cover all
  the vocabulary (all the words relevant for the
  language) and reflect the basic structure of a
  particular language (all the relations between
  words relevant for this particular language
  system).
• E.g. Kiss et al (1972) about 54.000 words,
  Nelson et al (1973-1990) about 75.000 words,
  Karaulov et al (1994-1998) 23.000 words

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What kind of linguistic information could be
extracted from WAT?

1. The core concepts of the language
2. Syntagmatic paradigmatic relations between
   words presented explicitly (as opposed to text
   corpora)
3. Relevance of word senses for native speakers
4. Relevance of relations for native speakers
5. Domain information that are shown (as opposed to
   dictionaries)
6. Semantic classification of words obtained by
   using formal criteria

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The core concepts of the language

• In every language there is a finite number of
  words that appear as responses more frequently
  then other words. This set is quite stable
• it does change much as the time goes
• it doesnt depends on the starting circumstances,
  e.g. on words that were chosen as stimulus words
• Russian man, house, love, life,
  be/eat, think, live, go, big/large,
  good, bad, no/not...
• 295 words with more then 100 relations
• English man, sex, no (not), love, house work,
  eat, think, go, live good, old, small
• 586 words with more then 100 relations
• Cf. EuroWordNet Basic Concepts

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Syntagmatic relations

• E.g. Cat -gt black, Cheshire, pussy
• Cat -gt mat, nip, purr
• Law of contiguity through life we learn what
  goes together and reproduce it together
• Right and left contexts of a word
• Help to acquire
• Selectional preferences, valency frames
• Semantic relations between words (e.g.
  ROLE/INVOLVED)
• Distinguishing different senses of a word
• Establishing relations of synonymy, hyponymy, and
  antonymy
• Cf. text corpora

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Paradigmatic relations

• E.g. Cat-gt dog, mouse, animal, pet
• Cat-gt eyes, claw
• Synonyms, hyponyms/hyperonyms/co-hyponyms,
  meronyms/holonyms, or antonyms
• Law of contiguity???
• Help us to acquire
• This information may be included directly in
  terms of semantic relations between wordnet
  entries
• Also it helps us to enrich and to check out the
  set of relations encoded earlier

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Classifying verbs according to the number of
their syntagmatic associations

• .

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Domain information

• E.g., hospital gt nurse, doctor, pain, ill,
  injury, load
• This type of data is not so easily extracted from
  corpora, in explanatory dictionaries it is
  presented partly
• Is crucial while we approach wordnet usage in IR.

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Relevance of word senses for native speakers

• WAT for each word 80 of associations are
  related to 1-3 of its senses.
• Cf. Corpus 90 of occurrences of a word
• That allows us
• to measure the relevance of a particular word
  sense for native speakers.
• to find an appropriate place for it in the
  hierarchy of senses.
• to define the necessary level of sense
  granularity to include into a wordnet no more
  and no less senses of each word than native
  speakers do differentiate.
• Problem emotionally coloured senses are thus
  overestimated. E.g. ???? ? ????

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Relevance of relations for native speakers

• It is clear that in a WN words must have at least
  a hyperonym and desirably a synonym.
• Other relations???
• Relations are not the same for different PoS, but
  also they are not the same for different words
  within the same PoS.
• E.g. buy CONVERSIVE sell, while cry
  INVOLVED_AGENT baby.

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WAT vs. Corpus

• Compare a corpus to WAT
• Wetter Rapp (1996), Willners (2001)
  Correlation between frequency of word X and word
  Y co-occurrence in a corpus and strength of
  association word X-word Y in WAT.
• Compare WAT to a corpus?

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WAT vs. Corpus (2)

• Coverage 64 word associations do not occur in
  the corpus

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WAT vs. Corpus (3)
N of occurrences in the corpus N of occurrences in RWAT of all word associations missing
0 2 48
0 3 22
0 4 14
0 5 8
0 6-10 5
0 11-15 lt1
0 15-20 lt1
0 gt 20 0
Table 1. Distribution of word associations that
do not occur in the corpus. NB! Mostly its
Syntagmatic WA that are missing, not
paradigmatic ones
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Conclusions

• The advantages of using WAT in wordnet
  constructing
• Great variety of linguistic information
  extracted.
• WAT is equal to or excels other LRs in
  several respects.
• Raw data (as opposed to theoretical one, cf.
  conventional dictionaries, that supposes the
  researchers introspection and intuition to be
  involved, and hence, leads to over- and
  under-estimation of the language phenomena).
• WAT is comparable to a balanced text corpus,
  and could supply all the necessary empirical
  information in case of absence of the latter.
• Probabilistic nature of data presented (data
  reflects the relative rather then absolute
  relevance of language phenomena).
• Parallel usage of WAT and other LR is effective
  way of
• constant checking-out of wordnet construction,
• refining wordnet and
• expanding wordnet

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Future plans

• WAT vs. Corpus vs. Wordnet
• Czech small large middle
• English large large large
• Russian large middle - small

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• Thank you