Paper: Techniques To Incorporate The Benefits Of A Hierarchy In A Modified Hidden Markov Model

ACL ID P06-2016
Title Techniques To Incorporate The Benefits Of A Hierarchy In A Modified Hidden Markov Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors
  • Lin-Yi Chou (University of Waikato, Hamilton New Zealand)

This paper explores techniques to take ad- vantage of the fundamental difference in structure between hidden Markov models (HMM) and hierarchical hidden Markov models (HHMM). The HHMM structure allows repeated parts of the model to be merged together. A merged model takes advantage of the recurring patterns within the hierarchy, and the clusters that exist in some sequences of observations, in order to increase the extraction accuracy. This paper also presents a new technique for re- constructing grammar rules automatically. This work builds on the idea of combining a phrase extraction method with HHMM to expose patterns within English text. The reconstruction is then used to simplify the complex structure of an HHMM The models discussed here are evaluated by applying them to natural langu...