@article {Ajallouda2022283,
	title = {KP-USE: An Unsupervised Approach for Key-Phrases Extraction from Documents},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {4},
	year = {2022},
	note = {cited By 4},
	pages = {283-289},
	abstract = {Automatic key-phrase extraction (AKE) is one of the most popular research topics in the field of natural language processing (NLP). Several techniques were used to extract the key-phrases: statistical, graph-based, classification algorithms, deep learning, and embedding techniques. AKE approaches that use embedding techniques are based on calculating the semantic similarity between a vector representing the document and the vectors representing the candidate phrases. However, most of these methods only give acceptable results in short texts such as abstracts paper, but on the other hand, their performance remains weak in long documents because it is represented by a single vector. Generally, the key phrases of a document are often expressed in certain parts of the document as, the title, the summary, and to a lesser extent in the introduction and the conclusion, and not of the entire document. For this reason, we propose in this paper KP-USE. A method extracts key-phrases from long documents based on the semantic similarity of candidate phrases to parts of the document containing keyphrases. KP-USE makes use of the Universal Sentence Encoder (USE) as an embedding method for text representation. We evaluated the performance of the proposed method on three datasets containing long papers, namely, NUS, Krapivin2009, and SemEval2010, where the results showed its performance outperforms recent AKE methods which are based on embedding techniques. {\textcopyright} 2022. All Rights Reserved.},
	keywords = {Deep learning, Embedding technique, Embeddings, Extraction, Graph-based classifications, Graphic methods, Key-phrase, Key-phrases extractions, Natural language processing systems, Performance, Research topics, Semantic similarity, Semantics, Signal encoding, Statistical graphs, Universal sentence encoder, Unsupervised approaches},
	doi = {10.14569/IJACSA.2022.0130433},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129921696\&doi=10.14569\%2fIJACSA.2022.0130433\&partnerID=40\&md5=880d07ce539281dced3a51a82de6a16c},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Lahmar, E.B.}
}
@conference {Ajallouda2020169,
	title = {K-means hac and fcm which clustering approach for arabic text?},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {169-174},
	abstract = {Today we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas opinions and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which in reality is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint clustering approaches such as K-mean HAC or FCM can be exploited. In this paper we present and compare these approaches. And to show the importance of exploiting clustering algorithms to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco which is the case of teachers contracting with academies. {\textcopyright} 2020 ACM.},
	keywords = {Arabic texts, Classification (of information), Classification approach, Clustering approach, Intelligent systems, Internet users, K-means clustering, Rapid growth, Real case, Textual data, Web resources},
	doi = {10.1145/3419604.3419779},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096415898\&doi=10.1145\%2f3419604.3419779\&partnerID=40\&md5=8aca383d8f485d15b3da191f03c5bf62},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@article {Anter2013158,
	title = {K-Schema: A new approach, based on the distribution of user queries, to create views to materialize in a hybrid integration system},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {47},
	number = {1},
	year = {2013},
	note = {cited By 2},
	pages = {158-170},
	abstract = {The explosion of information technologies and telecommunications has made easy the access and production of information. That is how a very large mass of the latter has generated. This situation has made the integration systems a major need. Among these systems, there is the hybrid mediator. The latter interrogates one part of data on demand as in the virtual approach while charging, filtering and storing the second part, as views, in a local database. The choice of this second part is a critical task. This paper presents a selective approach, which based, essentially, to create these views, on the queries previously posed on the system. Based on the distribution of previous user queries, our approach extract all data most queried by users. The obtained data are classified as candidate views for materialization. Then selecting which one to materialize among all those created in the first step. {\textcopyright} 2005 - 2013 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872374571\&partnerID=40\&md5=c84d7e37d38a4f230cdf4c3a43c22954},
	author = {Anter, S. and Zellou, A. and Idri, A.}
}
