2008 ModelingLatentDynamicinShallowP

• (Sun et al., 2008) ⇒ Xu Sun, Louis-Philippe Morency, Daisuke Okanohara, and Jun'ichi Tsujii. (2008). “Modeling Latent-dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference.” In: Proceedings of the 22nd International Conference on Computational Linguistics.

Subject Headings: LDCRM; Shallow Parsing

Cited By

• http://scholar.google.com/scholar?q=%22Modeling+latent-dynamic+in+shallow+parsing%3A+a+latent+conditional+model+with+improved+inference%22+2008
• http://dl.acm.org/citation.cfm?id=1599081.1599187&preflayout=flat#citedby

Quotes

Abstract

Shallow parsing is one of many NLP tasks that can be reduced to a sequence labeling problem. In this paper we show that the latent-dynamics (i.e., hidden substructure 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 algorithm, which is able to produce the most probable label sequence on latent conditional models. It outperforms two existing inference algorithms. With the BLP inference, the LDCRF model significantly outperforms CRF models on word features, and achieves comparable performance of the most successful shallow parsers on the CoNLL data when further using part-of-speech features.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ModelingLatentDynamicinShallowP	Daisuke Okanohara Jun'ichi Tsujii Louis-Philippe Morency Xu Sun			Modeling Latent-dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference