Mining Chinese comparative sentences by semantic role labeling

Author

Hou, Feng ; LI, Guo-hui

Author_Institution

Sch. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha

Volume

5

fYear

2008

fDate

12-15 July 2008

Firstpage

2563

Lastpage

2568

Abstract

This paper studies the problem of mining Chinese comparative sentences in text documents by using semantic role labeling (SRL). The comparative opinion can be divided into six semantic roles: holder, entity 1, comparative predicates, entity 2, attributes and sentiments. These six opinion elements were recognized and labeled by using SRL. A corpus of Chinese comparative sentences was manually labeled at first. Then a conditional random fields (CRFs) model was trained by learn from the corpus. Finally new comparative sentences were labeled by using this CRFs model, and comparative relations were extracted afterward.

Keywords

data mining; natural language processing; random processes; text analysis; Chinese comparative sentence mining; conditional random fields; semantic role labeling; text document; Banking; Conference management; Cybernetics; Data mining; Information management; Labeling; Machine learning; Machine learning algorithms; Management information systems; Natural languages; Comparative sentences; Conditional random fields; Opinion mining; Semantic role labeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620840

Filename

4620840