Abstract It is common practice in linguistics to classify words not only on the basis of their meanings but also on the basis of their co-occurrence with other words. Running through the whole Firthian tradition, for example, is the theme that “You shall know a word by the company it keeps” (Firth, 1957). On the one hand, bank co-occurs with words and expression such as money, notes, loan, account, investment, clerk, official, manager, robbery, vaults, working in a, its actions, First National, of England, and so forth. On the other hand, we find bank co-occurring with river, swim, boat, east (and of course West and South, which have acquired special meanings of their own), on top of the, and of the Rhine. (Hanks, 1987: 127) The search for increasingly delicate word classes is not new. In lexicography, for example, it goes back at least to the “verb patterns” described in Hornby’s Advanced Learner’s Dictionary (first edition 1948). What is new is that facilities for the computational storage and analysis of large bodies of natural language have developed significantly in recent years, so that it is now becoming possible to test and apply informal assertions of this kind in a more rigorous way, and to see what company our words do keep. The proposed statistical description has a large number of potentially important applications, including: (a) constraining the language model both for speech recognition and optical character recognition (OCR), (b) providing disambiguation cues for parsing highly ambiguous syntactic structures such as noun compounds, conjunctions, and prepositional phrases, (c) retrieving texts from large databases (e.g., newspapers, patents), (d) enhancing the productivity of computational linguists in compiling lexicons of lexico-syntactic facts, and (e) enhancing the productivity of lexicographers in identifying normal and conventional usage.
更多
原文链接
分享
引用
