Evaluation of Topic Segmentation Algorithms on Arabic Texts

被引：0

作者：

Faycal, Nouar ^{[1
]}

Hacene, Belhadef ^{[2
]}

机构：

[1] May 8th 1945 Guelma Univ, Management Sci Dept, Guelma, Algeria

[2] Univ Constantine 2 Abdelhamid Mehri, MISC Lab, Constantine, Algeria

来源：

2018 2ND INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE AND SPEECH PROCESSING (ICNLSP) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we are interested in the topic segmentation of Arabic texts. For this aim, we evaluate two based lexical cohesion algorithms: MinCutSeg and BayesSeg by using the Pk and WindowDiff metrics. To assess how well each algorithm works, each was applied on three datasets with longer texts from two different domains: transcribed multi-party conversations and written texts. After adaptation to the Arabic language, the test results show significant differences in performance depending on the types of documents. Keywords-Topic segmentation, multi-party conversation transcripts, minimum cut criterion, Bayesian model, Arabic language.

引用

页码：130 / 135

页数：6

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