Word Association Thesaurus as a Resource for Extending Semantic Networks

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Word Association Thesaurus as a Resource for
Extending Semantic Networks

• Anna Sinopalnikova1, 2, Pavel Smrz1
• 1Faculty of Informatics, Masaryk University
• Botanicka 68a, 602 00 Brno, Czech Republic
• 2Saint-Petersburg State University
• Universitetskaya 11, Saint-Petersburg, Russia
• anna, smrz_at_fi.muni.cz

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Overview

• Motivation
• Word Association and other notions of
  psycholinguistics
• WAT vs. Corpus
• Semantic Information from WAT
• core concepts, semantic primitives, syntagmatic
  and paradigmatic relations, domain information

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Types of Semantic Resources used in NLP
Corpora Dictionaries, thesauri, ontologies, taxonomies
These are primary resources, presenting (more or less) raw data on the language in use. Information is given implicitly. Need special extraction procedures and tools. These are derived resources, presenting explications of some internal knowledge. They are based on primary resources researchers intuition. Information is given explicitly.
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Motivation

• There is still a need for empirical basis of
  semantic network construction.
• Semantic Web initiatives.
• WAT are available for many languages. Nobody
  knows what are they good for and how to use them.

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Word Association and other notions of
psycholinguistics

• Word Association
• Word Association Test
• Word Association Norms
• Word Association Thesaurus

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Example

• Needle stimulates
• -gt thread 41, pin 13, sharp 6, sew 5,
  cotton 2, dressmaker 1, fix 1, prick 1,
  sewing 1, sow 1, spring 1, stitch 1, etc.

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WATs explored

• RAT - Russian WAT by Karaulov et al (1994-1998)
  8000 stimuli - 23000 words covered 1000
  subjects,
• EAT - Edinburgh WAT by Kiss et al (1972) 8400
  stimuli 54000 words covered - 1000 subjects,
• Czech WAN (Novak et al, 1996) 150 stimuli - 4000
  words covered 250 subjects.
• Experience gained in projects
• RussNet (a wordnet-like database for Russian
  linking lexical semantics with derivational
  morphology
• Czech part of the BalkaNet project (multilingual
  wordnet-like network for 5 Balkan languages and
  Czech).

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WAT vs. Corpus

• History Church Hanks, 1990 Wettler Rapp,
  1993 Willners, 2001
• Bokrjonok 3.0. - balanced corpus for Russian (16
  mln words),
• BNC - British National Corpus (112 mln),
• CNC - Czech National Corpus (160 mln) and its
  unbalanced version (630 mln words)
• Research procedure
• 5000 pairs e.g. cheese mouse, dark - alley have
  been extracted from each WAN in random order, and
  then searched in the corpora.
• The window span was fixed to -10 10 words.

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WAN vs. Corpus Russian

• Quantitative analysis (Sinopalnikova, 2004)
• - 64 word associations do not occur in the
  corpus,
• - 49 while excluding unique associations (that
  with
• absolute frequency 1)
• Qualitative analysis
• - high ratio of syntagmatic associations to be
  absent,
• - for verbs this number was up to 84.

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WAN vs. Corpus Russian (2)
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WAN vs. Corpus English

• Quantitative analysis
• - 31 word associations do not occur in the BNC
• Qualitative analysis
• PARADIGMATIC 57,1
• SYNTAGMATIC 8,4
• DOMAIN 21,7
• OTHER 12,8

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WAN vs. Corpus English (2)

• acquiring synonymy and hyponymy
• e.g. sex fornicate (archaic or humorous), ire
  (poetic) anger, cowardly yellow (slang)
• acquiring information about low frequent words
• e.g. perambulate (NBNC 3), fornicate (NBNC
  6)
• cf. EAT perambulate - walk 30, pram 17, baby
  9, push 8, about 1, dawdle 1,move 1,
  promenade 1, slowly 1, stroll1, through1,
  wander1, etc.
• acquiring domain relations absent portion of
  them was surprisingly large for such corpus as
  BNC
• e.g. ink-pot pen 24, non-violence peace 29,
  offside soccer 2

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WAN vs. Corpus Czech

• Quantitative analysis
• - 514 associations missing (10,28)
• Qualitative analysis
• - proportion of the syntagmatic and paradigmatic
  ones among them was similar to that for English

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Extracting semantic information from WAT

• Associations
• by form 10 (e.g. know no, yellow - mellow)
• by meaning 90 (e.g. needle sew, yellow -
  sun)
• core concepts,
• semantic primitives,
• syntagmatic and paradigmatic relations,
• domain information

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Core concepts

• In WAT there could be observed words that have an
  above-average number of direct links to other
  words.
• Russian ???????, ???, ???, ?????, ????, ??????,
  ????, ????, ???????, ??????, ?????, ??? (??),
  ?????, ?????? etc. (295 words with more then 100
  relations)
• English man, sex, no (not), love, house work,
  eat, think, go, live good, old, small etc. (586
  words with more then 100 relations)
• Czech clovek, dum, strom jíst, jít, myslet
  moc, starý, velký, bílý, hezký etc.
• These words determine the fundamental concepts of
  a particular language system, and thus should be
  incorporated into ontology as its core components
  (e.g., SUMO upper concepts or EWN Base Concepts.

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Semantic primitives

• WAT could also provide a list of basic concepts
  associated with each separate word.
• Thus revealing semantics of a word (situation) as
  a list of semantic constituents - separate pieces
  of information.
• Abstract words (verbs, adjectives or nouns
  denoting complex situation or emotional states)
  are difficult to decompose by means of logic and
  intuition.
• E.g. Depression could be reduced to its
  constituents sad 7, low 5, black 4, manic 4,
  sadness 3, bored 3, misery 2, tiredness 2,
  despair 1, gloom 1, grey 1, hopelessness 1,
  monotony 1, sick 1, mood 1, nerves 1, etc., its
  probable causes rain 3, guilt 1, pain 1,
  unemployment 1, its probable effects suicide 1,
  its antipodes elation 3, fun 1, happiness 1 etc.

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Syntagmatic and paradigmatic relations

• Linguistic substitutes for reality
• WA reflect the order of events in reality, the
  way objects are organized in the space, and the
  way human beings experience them.
• Associations by contiguity e.g. cry baby may be
  treated as a manifestation of syntagmatic
  relation between verb and its subject, while take
  hand as a ROLE_INSTRUMENT relation.
• Generalization! e.g. drink water, beer, milk,
  ale, Coca-cola, coffee, juice, etc. found in WAT
  should be generalized as drink ROLE_OBJECT
  beverage relation and in such a form incorporated
  in the semantic network

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Syntagmatic and paradigmatic relations (2)

• The law of contiguity could not explain all
  associations.
• Law of similarity, e.g. inanimate dead 39
  (SYNONYMY), seek find 56 (CAUSE relation), buy
  sell 56 (CONVERSIVE relation).
• One of the main benefits of WAT paradigmatic
  relations are given explicitly as opposed to
  other sources of empirical data (e.g. text
  corpora).

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Domain information

• WAT explicitly present the way common words are
  grouped together according to the fragments of
  reality they describe.
• E.g., hospital gt nurse, doctor, pain,
  ill, injury, load
• Types of domain relations
• name of domain (situation) domain member e.g.
  hospital nurse8, finance money 61, football
  player4 marriage husband 2
• participant participant e.g. pepper salt 58,
  tamer lion 69, needle thread 41 mouse
  cat 22
• participant circumstance e.g. umbrella rain
  58 actor stage23
• participant pointer to its function/role in the
  situation e.g. larder food 58, envelope
  letter 60, actor play 15 etc.
• To differentiate types of domain relations within
  semantic network, vs. to include them as uniform
  IS_ASSOCIATED_TO relation?

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Conclusions

• Advantages of using WAT in constructing semantic
  network
• Simplicity of data acquisition.
• Broad variety of semantic information to acquire.
• Empirical nature of data extracted (as opposed to
  theoretical one, cf. conventional ontologies,
  taxonomies or classification schemes, that
  supposes the researchers introspection and
  intuition to be involved, and hence, leads to
  over- and under-estimation of the phenomena under
  consideration).
• Probabilistic nature of data presented (data
  reflects the relative rather then absolute
  relevance of semantic relations in each
  particular case).

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• Thank you...