Toward a New Process for Candidate Key-Phrases Extraction

Ajallouda, Lahbib; Hourrane, Oumaima; Zellou, Ahmed; Benlahmar, El Habib

doi:10.1007/978-3-031-02447-4_48

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…The process of our method proposed in this article consists of six main steps, as shown in Figure 1. Where in the first step we extract the candidate keyphrases, to achieve this we adopted the approach proposed in [35]. The second step is dedicated to embedding the candidate keyphrases by the transformer model, while the paragraphs of the document will be embedded by the deep average network (DAN) model.…”

Section: Keyphrase Extraction and Generation Methods 31 Process Of Th...mentioning

confidence: 99%

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Present and absent keyphrases extraction: an approach based on sentence embedding

Ajallouda

Zellou

2022

IJEECS

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The automatic keyphrases extraction (AKE) of a document is any expression by which we can learn its content without having to read it. Keyphrases are exploited in natural language processing (NLP) applications. These phrases are often mentioned in the document but there may be some keyphrases that are not mentioned. In the field of AKE, researchers have exploited many techniques, such as statistical calculation, deep learning algorithms, graph representation, and sentence embedding techniques. Approaches that exploit embedding techniques calculate the similarity between a document and a candidate keyphrase, where similar phrases to the document are considered as keyphrases. Representing the document by a single vector makes its performance poor, especially in long documents. This is in addition to the inability of these methods to generate absent keyphrases. In order to overcome these problems, our paper proposes an unsupervised approach to AKE, based on the universal sentence encoder (USE) to represent candidate keyphrases and parts of the document probably containing keyphrases. Our method also generates keyphrases not mentioned in the text. We compared the performance of the proposed approach with other methods based on embedding techniques, where the results showed the superiority of our approach especially in long documents.

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Section: Keyphrase Extraction and Generation Methods 31 Process Of Th...mentioning

confidence: 99%

“…For this, many techniques have been used such as term frequency-inverse document frequency (TFIDF), N-Gram, and part-of-speech tagging (POST). The method which has been proposed in [35] gives acceptable results. Figure 2 presents the different steps in order to select candidate keyphrases.…”

Section: Candidate Keyphrasesmentioning

confidence: 99%

Present and absent keyphrases extraction: an approach based on sentence embedding

Ajallouda

Zellou

2022

IJEECS

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“…According to, [14] and [15], candidate keyphrases are terms that do not contain punctuation or stop words and have morphosyntactic structures "adjective* noun+", for example, ("Big data", "Computer engineering", etc.). Many techniques used to select candidate keyphrases, such as Part-Of-Speech, N-grams [16], and Noun-Phrase-Chunks [17].…”

Section: Keyphrases Extraction Processmentioning

confidence: 99%

“…The noun phrase form has been modified to take into account the VBN and VBG tags to extract the candidate phrase. There are also studies suggesting a complete process for extracting candidate key phrases [15].…”

Section: Pos Pattern Descriptionmentioning

confidence: 99%

A Systematic Literature Review of Keyphrases Extraction Approaches

Ajallouda

Fagroud

Zellou

et al. 2022

Int. J. Interact. Mob. Technol.

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The keyphrases of a document are the textual units that characterize its content such as the topics it addresses, its ideas, their field, etc. Thousands of books, articles and web pages are published every day. Manually extracting keyphrases is a tedious task and takes a lot of time. Automatic keyphrases extraction is an area of text mining that aims to identify the most useful and important phrases that give meaning to the content of a document. Keyphrases can be used in many Natural Language Processing (NLP) applications, such as text summarization, text clustering and text classification. This article provides a Systematic Literature Review (SLR) to investigate, analyze, and discuss existing relevant contributions and efforts that use new concepts and tools to improve keyphrase extraction. We have studied the supervised and unsupervised approaches to extracting keyphrases published in the period 2015-2022. We have also identified the steps most commonly used by the different approaches. Additionally, we looked at the criteria that should be evaluated to improve the accuracy of keyphrases extraction. Each selected approach was evaluated for its ability to extract keyphrases. Our findings highlight the importance of keyphrase extraction, and provide researchers and practitioners with information about proposed solutions and their limitations, which contributes to extract keyphrases in a powerful and meaningful way effective.

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