Recognizing location names from Chinese texts based on Max-Margin Markov Network

Author

Li, Lishuang ; Ding, Zhuoye ; Huang, Degen

Author_Institution

Dalian Univ. of Technol., Dalian

fYear

2008

fDate

19-22 Oct. 2008

Firstpage

1

Lastpage

7

Abstract

This paper presents a novel method of recognizing location names from Chinese texts based on max-margin Markov _Network (M³Net) owing to its ability to exploit very high dimensional feature spaces (using the kernel trick) while at the same time dealing with structured data compared with Support Vector Machine (SVM) and conditional random fields (CRFs). In our model, the character itself, character-based part-of-speech (POS) tag, the information whether a character appears in the location name characteristic word table and context information are extracted as the features. The F-measure is up to 90.57% based on 1-order M³Net which is better than that based on either SVM or CRFs in open test on MSRA dataset.

Keywords

Markov processes; character recognition; support vector machines; text analysis; Chinese text; F-measure; character-based part-of-speech tag; conditional random field; context information extraction; location name characteristic word table; location names recognition; max-margin Markov network; structured data; support vector machine; Information retrieval; Libraries; Markov random fields; Natural languages; Optical computing; Performance evaluation; Relational databases; Spatial databases; Testing; Text recognition; CRFs; M³Net; SVM; named entity recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Language Processing and Knowledge Engineering, 2008. NLP-KE '08. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4515-8

Electronic_ISBN

978-1-4244-2780-2

Type

conf

DOI

10.1109/NLPKE.2008.4906752

Filename

4906752