1. Using resources WordNet and the BNC

2. WordNet: History 1985: a group of psychologists and linguists start to develop a “lexical database” –Princeton University –theoretical basis: results from psycholinguistics and psycholexicology –What are properties of the “mental lexicon”?

3. Global organisation division of the lexicon into five categories: –Nouns –Verbs –Adjectives –Adverbs –function words (“probably stored separately as part of the syntactic component of language” [Miller et al.]

4. Global organization nouns: organized as topical hierarchies verbs: entailment relations adjectives: N-dimensional hyperspaces adverbs: N-dimensional hyperspaces [Miller et al.]: “Each of these lexical structures reflects a different way of categorizing experience; attempts to impose a single organizing principle on all syntactic categories would badly misrepresent the psychological complexity of lexical knowledge.”

5. Basic principles organize lexical information in terms of word meaning, rather than word forms –“In this respect, WordNet resembles athesaurus more than a dictionary,...” [Miller et al.] “... a word is a conventional association between a lexicalized concept and an utterance that plays a syntactic role.” –word form: refers to physical utterance or inscription –word meaning: refers to the lexicalized concept that a form can be used to express

6. Lexical semantics How are word meanings represented in WordNet? –synsets (synonym sets) as basic units –a word meaning is represented by simply listing the word forms that can be used to express it example: senses of board –a piece of lumber vs. a group of people assembled for some purpose –synsets as unambiguous designators: –{board, plank} vs. {board, committee}

7. Synsets synsets often sufficient for differential purposes –if an appropriate synonym is not available a short gloss may be used –e.g. {board, (a person’s meals, provided regularly for money)}

8. Lexical Relations in WordNet “WordNet is organized by semantic relations.” –It is characteristic of semantic relations that they are reciprocated; –if there is a semantic relation R between meaning {x, x’,...} and meaning {y, y’,...}, then there is a relation R’ between {y,y’,...} and {x, x’,...}.

9. Lexical relations: synonymy similarity of meaning –Leibniz: two expressions are synonymous if the substitution of one for the other never changes the truth value of a sentence in which the substitution is made such global synonymy is rare (it would be redundant) –synonymy relative to a context: two expressions are synonymous in a linguistic context C if the substitution of one for the other in C does not alter the truth value –consequence of this synonymy in terms of substitutability: words in different syntactic categories cannot be synonyms

10. Lexical relations: antonymy antonym of a word x is sometimes not-x, but not always –rich and poor are antonyms –but: not rich does not imply poor –(because many people consider them neither rich nor poor) antonymy is a lexical relation between word forms, not a semantic relation between word meanings –meanings {rise,ascend} and {fall, descend} are conceptual opposites, but they are not antonyms [rise/fall] and [ascend/descend] are pairs of antonyms –{w 1 w 2 }  S 1 & {w 3 w 4 }  S 2 & ant(w 1,w 3 )  ant(w 2,w 4 )

11. Lexcial relations: hyponymy hyponymy is a semantic relation between word meanings –{maple} is a hyponym of {tree} inverse: hypernymy –{tree} is a hypernym of {maple} also called: subordination/superordination; subset/superset; ISA relation test for hyponomy: –native speaker must accept sentences built from the frame “An x is a (kind of) y”

12. Lexcial relations: meronymy A concept represented by the synset {x, x’,...} is a meronym of a concept represented by the synset {y, y’,...} if native speakers of English accept sentences constructed from such frames as “A y has an x (as a part)”, “An x is a part of y”. inverse relation: holonymy HAS-AS-PART –part hierarchy –part-of is asymmetric and (with caution) transitive

13. Lexical relations: meronymy failures of transitivity caused by different part- whole relations, e.g. –A musician has an arm. –An orchestra has a musician. –but: ? An orchestra has an arm. Types of meronymy in WordNet: –component [most frequently found] –member –composition –phase process

14. WordNet’s noun hierarchy noun hierarchy partitioned into separate hierarchies with unique top hypernyms vague abstractions would be semantically empty, e.g. {entity} with immediate hyponyms {object, thing} and {idea}

15. {act,action,activity} {animal,fauna} {artifact} {attribute,property} {body,corpus} {cognition,knowledge} {communication} {event,happening} {feeling,emotion} {food} {group,collection} {location,place} {motive} {natural object} {natural phenomenon} {person,human being} {plant,flora} {possession} {process} {quantity,ammount} {relation} {shape} {state, condition} {substance} {time}

16. Nouns in WordNet noun hierarchy as lexical inheritance system –“... seldom goes more than ten levels deep, and the deepest examples usually contain technical levels that are not part of everyday vocabulary.” –Shetland pony → pony → horse → equid → odd-toed ungulate → herbivore → mammal → vertebrate → animal

17. Nouns in WordNet man-made artifacts: sometimes six or seven levels deep –roadster → car → motor vehicle → wheeled vehicle → vehicle → conveyance → artifact hierarchy of persons: about three or four levels –televangelist → evangelist → preacher → clergyman → spiritual leader → person Like all thesaurus structures, words can have multiple hypernyms

18. WordNets for other languages Idea has been widely copied Sometimes by “translating” Princeton WordNet –Lexical relations in general are universal... –But are they in practice? –Are synsets universal? EuroWordNet: combining multilingual WordNets to include cross-language equivalence –Inherent difficulties, as above

19. BNC One of the most widely used corpora (esp. in Britain, but also elsewhere) A balanced synchronic text corpus containing 100 million words (POS tagged) Collected in late 1980s 90% text, 10% transcribed speech Encoded according to TEI standards Associated tools (mainly for searching), but many users write their own (eg in Perl) http://www.natcorp.ox.ac.uk/

20. Using the BNC Just looking up words More interesting to construct queries that exploit the mark-up (see Allan’s slides) Already becoming dated (e.g. “numpty”) Results often contradict “authorities” such as dictionaries, especially in revealing primary senses/uses of words.

23. WWW as a corpus Standard Google search engine used with individual words does not always give good word collocations: after all, Google is document retrieval Try: http://labs1.google.com/sets

28. Lexical research Use corpus resource such as BNc together with WordNet to get interesting results → Allan’s slides