@conference {Ajallouda2022,
	title = {Doc2Vec, SBERT, InferSent, and USE Which embedding technique for noun phrases?},
	booktitle = {2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022},
	year = {2022},
	note = {cited By 4},
	abstract = {Phrase embedding is a technique of representing phrases in vector space. A very high effort has been made to develop this technique to improve tasking in various natural language processing (NLP) applications. The evaluation of phrase embedding has been presented in many studies, but most of them focused on the intrinsic or extrinsic evaluation process regardless of the type of the phrase (noun phrases, Verb phrases...). In the literature, there is no study evaluating the embedding of noun phrases, knowing that this type is used by many NLP applications, such as automatic key-phrase extraction (AKE), information retrieval, and question answering. In this article, we will present an empirical study to compare the most common phrase embedding techniques, to determine the most suitable for representing noun phrases. Dataset used in the comparison process consists of the noun phrases from the Inspec and SemEval2010 datasets, to which we have added their manually defined synonyms. {\textcopyright} 2022 IEEE.},
	keywords = {Embedding technique, Embeddings, Empirical studies, Key-phrases extractions, Natural language processing applications, Natural language processing systems, Noun phrase, Phrase embedding technique, Question Answering, Vector spaces},
	doi = {10.1109/IRASET52964.2022.9738300},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127985676\&doi=10.1109\%2fIRASET52964.2022.9738300\&partnerID=40\&md5=f274393a28334ba6d338a5dc4e3b5f79},
	author = {Ajallouda, L. and Najmani, K. and Zellou, A. and Benlahmar, E.H.}
}
@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.}
}
