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In this paper, a supervised learning technique for extracting keyphrases ofArabic documents is presented. The extractor is supplied with linguisticknowledge to enhance its efficiency instead of relying only on statisticalinformation such as term frequency and distance. During analysis, an annotatedArabic corpus is used to extract the required lexical features of the documentwords. The knowledge also includes syntactic rules based on part of speech tagsand allowed word sequences to extract the candidate keyphrases. In this work,the abstract form of Arabic words is used instead of its stem form to representthe candidate terms. The Abstract form hides most of the inflections found inArabic words. The paper introduces new features of keyphrases based onlinguistic knowledge, to capture titles and subtitles of a document. A simpleANOVA test is used to evaluate the validity of selected features. Then, thelearning model is built using the LDA - Linear Discriminant Analysis - andtraining documents. Although, the presented system is trained using documentsin the IT domain, experiments carried out show that it has a significantlybetter performance than the existing Arabic extractor systems, where precisionand recall values reach double their corresponding values in the other systemsespecially for lengthy and non-scientific articles.

Arabic Keyphrase Extraction using Linguistic knowledge and Machine Learning Techniques