Paper: Jointly Labeling Multiple Sequences: A Factorial HMM Approach

ACL ID P05-2004
Title Jointly Labeling Multiple Sequences: A Factorial HMM Approach
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
  • Kevin Duh (University of Washington, Seattle WA)

We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of part- of-speech tagging and noun phrase chunk- ing. The model is based on the Factorial Hidden Markov Model (FHMM) with dis- tributed hidden states representing part- of-speech and noun phrase sequences. We demonstrate that this joint labeling ap- proach, by enabling information sharing between tagging/chunking subtasks, out- performs the traditional method of tag- ging and chunking in succession. Fur- ther, we extend this into a novel model, Switching FHMM, to allow for explicit modeling of cross-sequence dependencies based on linguistic knowledge. We report tagging/chunking accuracies for varying dataset sizes and show that our approach is relatively robust to data sparsity.