Paper: Sequential Labeling with Latent Variables: An Exact Inference Algorithm and its Efficient Approximation

ACL ID E09-1088
Title Sequential Labeling with Latent Variables: An Exact Inference Algorithm and its Efficient Approximation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors
  • Xu Sun (University of Tokyo, Tokyo Japan)
  • Jun'ichi Tsujii (University of Tokyo, Tokyo Japan; University of Manchester, Manchester UK; National Center for Text Mining, UK)

Latent conditional models have become popular recently in both natural language processing and vision processing commu- nities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent con- ditional models by using efficient search strategy and dynamic programming. Fur- thermore, we describe a straightforward solution on approximating the LDI, and show that the approximated LDI performs as well as the exact LDI, while the speed is much faster. Our experiments demonstrate that the proposed inference algorithm out- performs existing inference methods on a variety of natural language processing tasks.