Paper: Modeling Latent-Dynamic in Shallow Parsing: A Latent Conditional Model with Imrpoved Inference

ACL ID C08-1106
Title Modeling Latent-Dynamic in Shallow Parsing: A Latent Conditional Model with Imrpoved Inference
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

Shallow parsing is one of many NLP tasks that can be reduced to a sequence la- beling problem. In this paper we show that the latent-dynamics (i.e., hidden sub- structure of shallow phrases) constitutes a problem in shallow parsing, and we show that modeling this intermediate structure is useful. By analyzing the automatically learned hidden states, we show how the latent conditional model explicitly learn latent-dynamics. We propose in this paper the Best Label Path (BLP) inference algo- rithm, which is able to produce the most probable label sequence on latent condi- tional models. It outperforms two existing inference algorithms. With the BLP infer- ence, the LDCRF model significantly out- performs CRF models on word features, and achieves comparable performance of the most successful s...