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Corpus-based linguistics and WordNet

Linguistics has traditionally focused on the phenomena of spoken language, and since Chomsky [REF716] [REF725] further focused on syntactic rules describing the generation and understanding of individual sentences. But as more large samples of written text have become increasingly available, CORPUS-BASED LINGUISTICS has become an increasingly active area of research . . D. D. Lewis and E. D. Liddy have collected a useful bibliography and resource list on NLP for IR , and R. Futrelle and X. Zhang have collected Large-Scale Persistent Object Systems For Corpus Linguistics and Information Retrieval . Stuart Shieber maintains the The Computation and Language Archive as part of the LANL reprint server.

The sophistication of computational linguistics syntactic analysis provides a striking contrast to IR's typical BAG-OF-WORDS approach which aggressively ignores any ordering effects. Conversely, IR's central concerns with semantic issues of meaning and the ultimate pragmatics of using language to find relevant documents goes beyond the myopic concern with isolated sentences that is typical of linguistics. The range of potential interactions between these perspectives is only beginning to be explored, but includes the introduction of parsing techniques with IR retrieval systems [Smeaton92] [Strzalkowski94] , as well as using statistical methods to identify PHRASES that are a first step from a simple bag-of-words to sytactically well-formed sentences [LEWIS92b] [Krovetz93] [Church89] [REF1112] [REF866] . Another important direction of interaction is the use of IR methods across multi-lingual corpora, for example arising from the integration of the European Community [Hull96a] [Sheridan96] .

From a syntactic perspective, the only way to get issues of real meaning into language is via the LEXICON : a dictionary of all words and their meanings. Our present concern, inter-keyword structures, them becomes an issue of LEXICAL SEMANTICS [REF641] , and it is no surprise then that linguists have also developed representational systems for inter-word relationships. An influential and widely used example of a keyword thesaurus is the WordNet {developed by George Miller and colleagues} [Fellbaum98] . {This is the same George Miller whose analysis of Zipf's Law was mentioned in Section §3.2 . He is perhaps most famous for his ``human information processing'' analyses of cognition, such as the limit of $7\pm2$ on the number of ``chunks'' that can be retained in short-term memory [Miller56] . The same information theoretic motivation underlies all these wide-ranging efforts.}

One obvious distinction of WordNet is simply the size of its vocabulary: it contains almost 100,000 distinct word forms, divided into lexical categories as shown in Figure (FOAref) . Central to the lexical approach to semantics is distinguishing between lexical items and the ``concepts'' they are meant to invoke. ``Word form'' will be used here to refer to the physical utterance or inscription and ``word meaning'' to refer to the lexicalized concept that a form can be used to express. Then the starting point for lexical semantics can be said to be the mapping between forms and meanings. [Fellbaum98]

The relations connecting words in WordNet ar similar to those used within thesauri, but not identical. The first and most important relation is SYNONYMY . This has a special role in WordNet, pulling multiple word forms together into a SYNONYM SET which, by definition, all have the same meaning. According to one definition (usually attributed to 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\ldots. A weakened version of this definition would make 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. [Fellbaum98]

The BT/NT relation in standard thesauri is refined in WordNet into two types of relations, HYPERNYMY and MERONYMY . The former relation plays a dominant role, allowing INHERITANCE of various properties of parent words by their children. Much attention has been devoted to hyponymy/hypernymy (variously called subordination/superordination, subset/superset, or the ISA relation)\ldots. A hyponym inherits all the features of the more generic concept and adds at least one feature that distinguishes it from its superordinate and from any other hyponyms of that superordinate. This convention provides the central organizing principle for the nouns in WordNet. [Fellbaum98] This hypernymy relation connects virtually all the words into a forest of trees rooted on a very restricted set of ``unique beginners.'' In the case of nouns, the top-level categories are those shown in Figure (FOAref) , and for verbs in Figure (FOAref)

The final category of STATIVE VERBS is used to capture the distinction between the majority of ACTIVE VERBS and those (e.g., SUFFICE, BELONG, RESEMBLE) reflecting state characteristics.

WordNet also represents roughly the opposite of the synonym relation with the ANTONYMY relation. Defining this logically proves more difficult, and Miller is forced to simply equate it with human subjects' typical responses: Antonymy is a lexical relation between word forms, not a semantic relation between word meanings\ldots. The strongest psycholinguistic indication that two words are antonyms is that each is given on a word association test as the most common response to the other. For example, if people are asked for the first word they think of (other than the probe word itself) when they hear VICTORY, most will respond DEFEAT; when they hear DEFEAT most will respond VICTORY. [Fellbaum98]

The use of the antonymy relation in WordNet is particularly interesting when applied to adjectives. The semantic organization of descriptive adjectives is entirely different from that of nouns. Nothing like the hyponymic relation that generates nominal hierarchies is available for adjectives\ldots. The semantic organization of adjectives is more naturally thought of as an abstract hyperspace of N dimensions rather than as a hierarchical tree. [Fellbaum98] First, WordNet distinguishes the bulk of adjectives, which are called DESCRIPTIVE ADJECTIVES (such as BIG, INTERESTING, POSSIBLE) from RELATIONAL ADJECTIVES (PRESIDENTIAL, NUCLEAR) and REFERENCE-MODIFYING ADJECTIVES (FORMER, ALLEGED). They then find: All descriptive adjectives have antonyms; those lacking direct antonyms have indirect antonyms, i.e., are synonyms of adjectives that have direct antonyms. (p. 28) An example of the resulting dumbbell-shaped ``bi-polar'' organization is shown in Figure (figure) .

Voorhees has been one of the first to explore how WordNet data can be harnassed as part of a search engine [Voorhees93] .

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