Fertility Models for Statistical Natural Language Understanding (1997)
| Venue: | In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics |
| Citations: | 9 - 0 self |
BibTeX
@INPROCEEDINGS{Pietra97fertilitymodels,
author = {Stephen Della Pietra and Mark Epstein and Salim Roukos and Todd Ward},
title = {Fertility Models for Statistical Natural Language Understanding},
booktitle = {In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics},
year = {1997},
pages = {168--173}
}
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Abstract
Several recent efforts in statistical nat- urM language understanding (NLU) have focused on generating clumps of English words from semantic meaning concepts (Miller et al., 1995; Levin and Pieraccini, 1995; Epstein et al., 1996; Epstein, 1996). This paper extends the IBM Ma- chine Translation Group's concept of fertil- ity (Brown et al., 1993) to the generation of clumps for natural language understanding. The basic underlying intuition is that a single concept may be expressed in English as many disjoint clump of words. We present two fertility models which attempt to capture this phenomenon. The first is a Poisson model which leads to appealing computational simplicity. The second is a general nonparametric fertility model. The general model's parameters are bootstrapped from the Poisson model and updated by the EM algorithm. These fertility models can be used to impose clump fertility structure on top of preexisting clump generation models. Here, we present resuits for adding fertility structure to unigram, bigram, and headword clump generation models on ARPA's Air Travel Infor- mation Service (ATIS) domain.
