In recent years there is much interest in word cooccurrence relations, such as n-grams, verb-object combinations, or cooccurrence within a limited context. This paper discusses how to estimate the probability of cooccurrences that do not occur in the training data. We present a method that makes local analogies between each specific unobserved cooccurrence and other cooccurrences that contain similar words, as determined by an appropriate word similarity metric. Our evaluation suggests that this method performs better than existing smoothing methods, and may provide an alternative to class based models.
|Number of pages||8|
|Journal||Proceedings of the Annual Meeting of the Association for Computational Linguistics|
|State||Published - 1993|
|Event||31st Annual Meeting of the Association for Computational Linguistics, ACL 1993 - Columbus, United States|
Duration: 22 Jun 1993 → 26 Jun 1993
Bibliographical notePublisher Copyright:
© 1993 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All rights reserved.