TY  - JOUR
T1  - Using Semantic Similarity with Word Embeddings for
Arabic Multi-Words Term Extraction
AU - Lamrani, El-Khadir AU - Ben Lahmer, El Habib AU - Marzak, Abdelaziz 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 23
SP  - 10092
EP  - 10100
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.10092.10100
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.10092.10100
KW  - Multiword terms extraction
KW  -features extraction
KW  -linguistic filtering
KW  -semantic similarity
KW  -word
embedding
KW  -Arabic textes
AB  - Identifying and extract terms from textual source is an indispensable task in information retrival and
question answering systems by experiments multi-word terms represent the best candidates to represent a
specific domain in Arabic. In this research, we assumed that the Multi-Word Terms (MWTs) consist of words
with similar contextual representations and we propose a hybrid method of extracting multi-word terms from
Arabic texts combines between linguistic and semantic approach, based on word embeddings which we use
a linguistic and morphosyntactic analysis of the Arabic language to find candidate terms and we use cosine
similarity between distributed representation of words for ranking candidate terms. The proposed methodology
has been tested in a case studies carried out in the environnemental domains with promising results.
ER  -