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Indexed by:期刊论文

Date of Publication:2010-07-01

Journal:Journal of Information and Computational Science

Included Journals:EI、Scopus

Volume:7

Issue:7

Page Number:1535-1541

ISSN No.:15487741

Abstract:In the fields of natural language processing such as information processing and machine translation, recognizing simple and non-recursive Chinese base phrases is an important task. In stead of rule-based model, we adopt the statistical machine learning method, newly proposed Latent semi-CRF model to solve the Chinese base phrase chunking problem. The Chinese base phrases could be treated as the sequence labeling problem, which involve the prediction of a class label for each frame in an unsegmented sequence. The Chinese base phrases have sub-structures which could not be observed in training data. Latent semi-CRF, which incorporates the advantages of Latent Dynamic Conditional Random Fields and semi-CRF that model the sub-structure of a class sequence and learn dynamics between class labels, in detecting the Chinese base phrases. Our results demonstrate that the latent dynamic discriminative model compares favorably to Support Vector Machines, Maximum Entropy Model, and Conditional Random Fields (including LDCRF and semi-CRF) on Chinese base phrases chunking. Copyright ? 2010 Binary Information Press.