Abstract
In many applications of natural language processing it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations "eat a peach" and "eat a beach" is more likely. Statistical NLP methods determine the likelihood of a word combination according to its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in a given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on "most similar" words. We describe a probabilistic word association model based on distributional word similarity, and apply it to improving probability estimates for unseen word bigrams in a variant of Katz's back-off model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error.
| Original language | English |
|---|---|
| Pages (from-to) | 272-278 |
| Number of pages | 7 |
| Journal | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
| Volume | 1994-June |
| State | Published - 1994 |
| Externally published | Yes |
| Event | 32nd Annual Meeting of the Association for Computational Linguistics, ACL 1994 - Las Cruces, United States Duration: 27 Jun 1994 → 30 Jun 1994 |
Bibliographical note
Publisher Copyright:© 1994, Association for Computational Linguistics.