Paper: Fertility Models For Statistical Natural Language Understanding

ACL ID P97-1022
Title Fertility Models For Statistical Natural Language Understanding
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997
Authors

Several recent efforts in statistical nat- ural language understanding (NLU) have focused on generating clumps of English words from semantic meaning concepts (Miller et al. , 1995; Levin and Pierac- cini, 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 understand- ing. The basic underlying intuition is that a single concept may be expressed in Eng- lish 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 appeal- ing computational simplicity. The second is a general nonparametric fertility model. The general model's parameters are boot- strapped f...