A hybrid Approach for Arabic Multi-Word Term Extraction

Building a domain model from a specialized corpus requires identifying candidate terms. It also includes identifying semantic relations between terms. Once this model is constructed it can be used for many tasks of information retrieval. In this process, multi-word terms have a great importance. In the one hand they constitute domain relevant candidate terms. On the other hand syntactic relations that link their constituents can be used to infer semantic relations between terms. In this paper we propose to extract mutli-word terms from Arabic specialized corpora. The proposed approach uses linguistic rules based on morphological features and POS (Part Of Speech) tags to parse documents and retrieve candidate terms. Statistical measures are used to deal with ambiguities generated by the linguistic tools and to rank candidate terms according to their relevance. We present experiments on a corpus from the environment domain. We report high quality results that are confirm the targets set for the precision metric.