1994 CoOccurrenceVectorsfromCorporaV

(Niwa & Nitta, 1994) ⇒ Yoshiki Niwa, and Yoshihiko Nitta. (1994). “Co-occurrence Vectors from Corpora Vs. Distance Vectors from Dictionaries.” In: Proceedings of the 15th conference on Computational linguistics - Volume 1. doi:10.3115/991886.991938

Subject Headings:

Notes

Cited By

Quotes

Abstract

A comparison was made of vectors derived by using ordinary co-occurrence statistics from large text corpora and of vectors derived by measuring the interword distances in dictionary definitions. The precision of word sense disambiguation by using co-occurrence vectors from the 1987 Wall Street Journal (20M total words) was higher than that by using distance vectors from the Collins English Dictionary (60K head words + 1.6M definition words). However, other experimental results suggest that distance vectors contain some different semantic information from co-occurrence vectors.

1. Introduction

…

3 Co-occurrence Vectors

We use ordinary co-occurrence statistics and measure the co-occurrence likelihood between two words, X and Y, by the mutual information estimate (Church and Hanks, 1989): :[math]\displaystyle{ I(\mathbf{X},\mathbf{Y}) = \log^+ \frac{P(\mathbf{X} \mid \mathbf{Y})}{P(\mathbf{X}}) }[/math], where P(X) is the occurrence density of word X in a whole corpus, and the conditional probability [math]\displaystyle{ P(\mathbf{X} \mid \mathbf{Y}) }[/math] is the density of word X in a neighborhood of word Y. Here the neighborhood is defined as 50 words before or after any appearance of word Y. (There is a variety of neighborhood definitions such as "100 surrounding words" (Yarowsky 1992) and "within a distance of no more thall 3 words ignoring function words" (Dagan et al., 1993).)

…

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1994 CoOccurrenceVectorsfromCorporaV	Yoshiki Niwa Yoshihiko Nitta			Co-occurrence Vectors from Corpora Vs. Distance Vectors from Dictionaries				10.3115/991886.991938		1994