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Published on December 3, 2007

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Word Association Thesaurus as a Resource for Building Wordnet:  Word Association Thesaurus as a Resource for Building Wordnet Anna Sinopalnikova Masaryk University, Brno, Czech Republic Saint-Petersburg State University, Russia anna@fi.muni.cz Overview :  Overview Types of LRs used What is Word Association? Information to be extracted from WAT WAT vs. Corpus Conclusions Future plans What kind of language resources are used to build wordnets?:  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 What is better?:  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! Word Association:  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 Association: examples (1):  Association: examples (1) Kill  Association: examples (1):  Association: examples (1) Kill   Bill Association: examples (2):  Association: examples (2) Association: examples (2):  Association: examples (2)  Nike Association: examples (3):  Association: examples (3) Association: examples (3):  Association: examples (3)  Kill Bill Word Association Test :  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. ‘Cat’ stimulates:  ‘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) Word Association Norms (WAN):  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 Word Association Thesaurus (WAT):  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 What kind of linguistic information could be extracted from WAT?:  What kind of linguistic information could be extracted from WAT? The core concepts of the language Syntagmatic & paradigmatic relations between words presented explicitly (as opposed to text corpora) Relevance of word senses for native speakers Relevance of relations for native speakers Domain information that are shown (as opposed to dictionaries) Semantic classification of words obtained by using formal criteria The core concepts of the language :  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 doesn’t 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 Syntagmatic relations :  Syntagmatic relations E.g. Cat -> black, Cheshire, pussy; Cat -> 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 Paradigmatic relations:  Paradigmatic relations E.g. Cat-> dog, mouse, animal, pet; Cat-> 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 Classifying verbs according to the number of their syntagmatic associations:  Classifying verbs according to the number of their syntagmatic associations . Domain information :  Domain information E.g., hospital –> 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. Relevance of word senses for native speakers:  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. дать – в рожу Relevance of relations for native speakers:  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. WAT vs. Corpus:  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? WAT vs. Corpus (2):  WAT vs. Corpus (2) Coverage: 64% word associations do not occur in the corpus WAT vs. Corpus (3):  WAT vs. Corpus (3) Table 1. Distribution of word associations that do not occur in the corpus. NB! Mostly it’s Syntagmatic WA that are missing, not paradigmatic ones Conclusions:  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 researcher’s 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 Future plans:  Future plans WAT vs. Corpus vs. Wordnet Czech: small – large – middle English: large – large – large Russian: large – middle - small Slide29:  Thank you…

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