@inproceedings{d0f43638808b4263af5109cd4e8973b2,
title = "AKEA: An Arabic keyphrase extraction algorithm",
abstract = "Keyphrase extraction is a critical step in many natural language processing and Information retrieval applications. In this paper, we introduce AKEA, a keyphrase extraction algorithm for single Arabic documents. AKEA is an unsupervised algorithm as it does not need any type of training in order to achieve its task. We rely on heuristics that collaborate linguistic patterns based on Part-Of-Speech (POS) tags, statistical knowledge, and the internal structural pattern of terms (i.e. word-occurrence). We employ the usage of Arabic Wikipedia to improve the ranking (or significance) of candidate keyphrases by adding a confidence score if the candidate exist as an indexed Wikipedia concept. Experimental results show that on average AKEA has the highest precision value, the highest F-measure value which indicates it presents more accurate results compared to its other algorithms.",
keywords = "Keyphrase extraction, Natural language processing",
author = "Eslam Amer and Khaled Foad",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016 ; Conference date: 24-10-2016 Through 26-10-2016",
year = "2016",
month = oct,
day = "18",
doi = "10.1007/978-3-319-48308-5_14",
language = "English",
isbn = "9783319483078",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "137--146",
editor = "Hassanien, {Aboul Ella} and Khaled Shaalan and Azar, {Ahmad Taher} and Tarek Gaber and Tolba, {Mohamed F.}",
booktitle = "Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016",
address = "Germany",
}