research-article

Hidden data states-based complex terminology extraction from textual web data model

Authors:

Mohamed Nazih OmriAuthors Info & Claims

Applied Intelligence, Volume 50, Issue 6

Pages 1813 - 1831

https://doi.org/10.1007/s10489-019-01568-4

Published: 01 June 2020 Publication History

Abstract

In order to respect the standards of the “semantic web” which allows the data to be shared and reused between several applications, it became necessary to model web text documents with a vision based on the concepts and exploit available linguistic resources. It’s evident that the extraction of semantic tokens ensures semantic modelling of web documents. Unfortunately, terminology extraction techniques from unstructured Web text remain unable to provide powerful results. Indeed, systems developed based on the classical techniques extract massively high amounts of candidate terms and leave the task of separation between relevant and irrelevant candidates for post-processing. In this paper, we introduce HMM-Extract a novel model for terminology retrieval based on Markov model. Our model integrates two modules that work in cascade: a module based on Hidden Markov Model (HMM) for complex term extraction and a module based on Markov Chain for filtering terms provided by the HMM. Thus, we try to focus on three main contributions: firstly, we provide a linguistic and statistical specification of relevant terms. Secondly, we show the possibility of using a HMM to extract relevant terms from unstructured textual documents. Finally, we prove the importance of integrating statistical knowledge in a Markov Chain and we show, experimentally, its contribution to the field of terminology extraction.

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Fkih F(2022)Similarity measures for Collaborative Filtering-based Recommender SystemsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.09.01434:9(7645-7669)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2021.09.014
Mahmoud RBelgacem SOmri M(2022)Deep signature-based isolated and large scale continuous gesture recognition approachJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.08.01734:5(1793-1807)Online publication date: 1-May-2022
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Index Terms

Hidden data states-based complex terminology extraction from textual web data model
1. Applied computing
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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cover image Applied Intelligence

Applied Intelligence Volume 50, Issue 6

Jun 2020

323 pages

ISSN:0924-669X

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© Springer Science+Business Media, LLC, part of Springer Nature 2020.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2020

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Cited By

Shu ZLi X(2022)Automatic Extraction of Web Page Text Information Based on Network Topology Coincidence DegreeWireless Communications & Mobile Computing10.1155/2022/92206612022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9220661
Fkih F(2022)Similarity measures for Collaborative Filtering-based Recommender SystemsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.09.01434:9(7645-7669)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2021.09.014
Mahmoud RBelgacem SOmri M(2022)Deep signature-based isolated and large scale continuous gesture recognition approachJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.08.01734:5(1793-1807)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2020.08.017
Mahmoud RBelgacem SOmri M(2022)Towards an end-to-end isolated and continuous deep gesture recognition processNeural Computing and Applications10.1007/s00521-022-07165-w34:16(13713-13732)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s00521-022-07165-w

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