NDLI: Chinese base phrases chunking based on latent semi-CRF model

Content Provider	IEEE Xplore Digital Library
Author	Xiao Sun Xiaoli Nan
Copyright Year	2010
Description	Author affiliation: School of Computer Science and Engineering, Dalian Nationalities University, Liaoning, China (Xiao Sun) \|\| Department of Economics, Dalian University of Technology, Liaoning, China (Xiaoli Nan)
Abstract	In the fields of Chinese natural language processing, recognizing simple and non-recursive base phrases is an important task for natural language processing applications, such as information processing and machine translation. Instead 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. We propose a latent discriminative model called Latent semi-CRF(Latent Semi Conditional Random Fields), which incorporates the advantages of LDCRF(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.
Starting Page	1
Ending Page	7
File Size	356978
Page Count	7
File Format	PDF
ISBN	9781424468966
e-ISBN	9781424468997
DOI	10.1109/NLPKE.2010.5587802
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural Language Processing Feature extraction Educational institutions Latent semi-CRF Chinese Base Phrases Chunking
Content Type	Text
Resource Type	Article

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