Paper: Minimal Commitment And Full Lexical Disambiguation: Balancing Rules And Hidden Markov Models

ACL ID W00-0722
Title Minimal Commitment And Full Lexical Disambiguation: Balancing Rules And Hidden Markov Models
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors

In this paper we describe the construction of a part-of-speech tagger both for medical doc- ument retrieval purposes and XP extraction. Therefore we have designed a double system: for retrieval purposes, we rely on a rule-based ar- chitecture, called minimal commitment, which is likely to be completed by a data-driven tool (HMM) when full disambiguation is necessary.