Jointly Labeling Multiple Sequences: A Factorial HMM Approach

We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of partof-speech tagging and noun phrase chunking. The model is based on the Factorial Hidden Markov Model (FHMM) with distributed hidden states representing partof-speech and noun phrase sequences. We demonstrate that this joint labeling approach, by enabling information sharing between tagging/chunking subtasks, outperforms the traditional method of tagging and chunking in succession. Further, 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.