@article{MAKHILLJEAS2018132317232,
    title = {Using Semantic Similarity with Word Embeddings for
Arabic Multi-Words Term Extraction},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {23},
    pages = {10092-10100},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.10092.10100},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.10092.10100},
    author = {El-Khadir,El Habib and},
    keywords = {Multiword terms extraction,features extraction,linguistic filtering,semantic similarity,word
embedding,Arabic textes},
    abstract = {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.}
    }