Title: Word Association Thesaurus as a Resource for Building Wordnet
1Word 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
2Overview
- Types of LRs used
- What is Word Association?
- Information to be extracted from WAT
- WAT vs. Corpus
- Conclusions
- Future plans
3What 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
4What 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!
5Word 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
6Association examples (1)
7Association examples (1)
8Association examples (2)
9Association examples (2)
10Association examples (3)
11Association examples (3)
12Word 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.
13Cat 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)
14Word 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
15Word 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
16What 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
17The 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
18Syntagmatic 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
19Paradigmatic 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
20Classifying verbs according to the number of
their syntagmatic associations
21Domain 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.
22Relevance 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. ???? ? ????
23Relevance 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.
24WAT 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?
25WAT vs. Corpus (2)
- Coverage 64 word associations do not occur in
the corpus
26WAT 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
27Conclusions
- 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
28Future plans
- WAT vs. Corpus vs. Wordnet
- Czech small large middle
- English large large large
- Russian large middle - small
29