Chunking Arabic Texts Using Conditional Random Fields

被引：0

作者：

Khoufi, Nabil ^{[1
]}

Aloulou, Chafik ^{[1
]}

Hadrich Belguith, Lamia ^{[1
]}

机构：

[1] Univ Sfax, FSEGS, MIRACL Lab, ANLP Res Grp, Sfax, Tunisia

来源：

2014 IEEE/ACS 11TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) | 2014年

关键词：

Chunking; Arabic language; CRF; supervised learning;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Chunking or shallow syntactic parsing is proving to be a task of interest to many natural language processing applications. The problem gets worse for the Arabic language because of its specific features that make it quite different and even more ambiguous than other natural languages when processed. In this paper, we present a method for chunking Arabic texts based on supervised learning. We use the Conditional Random Fields algorithm and the Penn Arabic Treebank to train the model. For the experimentation, we use over than 10,100 sentences as training data and 2,524 sentences for the test. The evaluation of the method consists of the calculation of the generated model accuracy and the results are very encouraging.

引用

页码：428 / 432

页数：5

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