Multi-word Term Extraction Based on New Hybrid Approach for Arabic Language

Arabic Multiword Term are relevant strings of words in text documents. Once they are automatically extracted, they can be used to increase the performance of any text mining applications such as Categorisation, Clustering, Information Retrieval System, Machine Translation, and Summarization, etc. This paper introduces our proposed Multiword term extraction system based on the contextual information. In fact, we propose a new method based a hybrid approach for Arabic Multiword term extraction. Like other method based on hybrid approach, our method is composed by two main steps: the Linguistic approach and the Statistical one. In the first step, the Linguistic approach uses Part Of Speech (POS) Tagger (Taani’s Tagger) and the Sequence Identifier as patterns in order to extract the candidate AMTWs. While in the second one which includes our main contribution, the Statistical approach incorporates the contextual information by using a new proposed association measure based on Termhood and Unithood for AMWTs extraction. To evaluate the efficiency of our proposed method for AMWTs extraction, this later has been tested and compared using three different association measures: the proposed one named NTC-Value, NC-Value, and C-Value. The experimental results using Arabic Texts taken from the environment domain, show that our hybrid method outperforms the other ones in term of precision, in addition, it can deal correctly with tri-gram Arabic Multiword terms.