文章基本信息

标题：Effective Unsupervised Arabic Word Stemming: Towards an Unsupervised Radicals Extraction
本地全文：下载
作者：Ahmed Khorsi
期刊名称：The International Arab Journal of Information Technology
印刷版ISSN：1683-3198
出版年度：2012
卷号：9
期号：6
出版社：Zarqa Private University
摘要：This paper presents a new totally unsupervised and 90% effective stemming approach for classical Arabic. This stemming is meant to be a preparatory step to an unsupervised root (i.e., radicals) extraction. As a learning input, our stemming system requires no linguistic knowledge but a plain classical Arabic text. Once the learning input analyzed, our stemming system is able to extract the strongest segment of a given length, namely the stem. We start by a definition of the targeted stem, then, we show how our system performs about 90% true positives after a leaning of less than 15000 words. Unlike the other unsupervised approaches, ours does not suppose the perfectness of the input text and deals efficiently with the eventual (practically very frequent) misspellings. The test corpus we have used is an ultimate reference in the classical Arabic and its labeling has been rigorously done by a team of experts.
关键词：Computational morphology; machine learning; natural language processing; classical arabic; and semitic languages.