@article {10.3844/jcssp.2011.6.11,
article_type = {journal},
title = {An Automatic Collocation Extraction from Arabic Corpus},
author = {Saif, Abdulgabbar Mohammad and Aziz, Mohd J.A.},
volume = {7},
number = {1},
year = {2010},
month = {Dec},
pages = {6-11},
doi = {10.3844/jcssp.2011.6.11},
url = {https://thescipub.com/abstract/jcssp.2011.6.11},
abstract = {Problem statement: The identification of collocations is very important part in natural
language processing applications that require some degree of semantic interpretation such as, machine
translation, information retrieval and text summarization. Because of the complexities of Arabic, the
collocations undergo some variations such as, morphological, graphical, syntactic variation that
constitutes the difficulties of identifying the collocation. Approach: We used the hybrid method for
extracting the collocations from Arabic corpus that is based on linguistic information and association
measures. Results: This method extracted the bi-gram candidates of Arabic collocation from corpus
and evaluated the association measures by using the n-best evaluation method. We reported the
precision values for each association measure in each n-best list. Conclusion: The experimental results
showed that the log-likelihood ratio is the best association measure that achieved highest precision.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}