  In this paper, a supervised learning technique for extracting keyphrases of
Arabic documents is presented. The extractor is supplied with linguistic
knowledge to enhance its efficiency instead of relying only on statistical
information such as term frequency and distance. During analysis, an annotated
Arabic corpus is used to extract the required lexical features of the document
words. The knowledge also includes syntactic rules based on part of speech tags
and 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 represent
the candidate terms. The Abstract form hides most of the inflections found in
Arabic words. The paper introduces new features of keyphrases based on
linguistic knowledge, to capture titles and subtitles of a document. A simple
ANOVA test is used to evaluate the validity of selected features. Then, the
learning model is built using the LDA - Linear Discriminant Analysis - and
training documents. Although, the presented system is trained using documents
in the IT domain, experiments carried out show that it has a significantly
better performance than the existing Arabic extractor systems, where precision
and recall values reach double their corresponding values in the other systems
especially for lengthy and non-scientific articles.
