Hierarchical hidden conditional random fields for information extraction

Authors:
Satoshi Kaneko;Akira Hayashi;Nobuo Suematsu;Kazunori Iwata
Affiliations:
Graduate School of Information Sciences, Hiroshima City University, Asaminami-ku, Hiroshima, Japan;Graduate School of Information Sciences, Hiroshima City University, Asaminami-ku, Hiroshima, Japan;Graduate School of Information Sciences, Hiroshima City University, Asaminami-ku, Hiroshima, Japan;Graduate School of Information Sciences, Hiroshima City University, Asaminami-ku, Hiroshima, Japan
Venue:
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Year:
2011

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Abstract

Hidden Markov Models (HMMs) are very popular generative models for time series data. Recent work, however, has shown that for many tasks Conditional Random Fields (CRFs), a type of discriminative model, perform better than HMMs. Information extraction is the task of automatically extracting instances of specified classes or relations from text. A method for information extraction using Hierarchical Hidden Markov Models (HHMMs) has already been proposed. HHMMs, a generalization of HMMs, are generative models with a hierarchical state structure. In previous research, we developed the Hierarchical Hidden Conditional Random Field (HHCRF), a discriminative model corresponding to HHMMs. In this paper, we propose information extraction using HHCRFs, and then compare the performance of HHMMs and HHCRFs through an experiment.