Abstract-Word Sense Disambiguation (WSD) consists of identifying the correct sense of an ambiguous word occurring in a given context. Most of Arabic WSD systems are based generally on the information extracted from the local context of the word to be disambiguated. This information is not usually sufficient for a best disambiguation. To overcome this limit, we propose an approach that takes into consideration, in addition to the local context, the global context too extracted from the full text. More particularly, the sense attributed to an ambiguous word is the one of which semantic proximity is more close both to its local and global context. The experiments show that the proposed system achieved an accuracy of 74%.

No abstract

No abstract

In the context of Information Retrieval System (IRS), semantic coherence between text and the terms chosen to represent them, provides precision in the answers returned to the user. So, for the improvement of the capacity of these systems, it is necessary to design and develop methods based on a semantic text processing, for choosing the appropriate terms, which can represent semantically the contents of this text. We will be interested by this area of research in this paper, more particularly, we propose a method, allowing the extraction of concepts which represent the semantic content of an Arabic text. These concepts are extracted, from Arabic WordNet (AWN), which we apply for their, afterward, Formal concept Analysis, to produce a set of concepts, more reduced and more relevant.