UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method
Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supe...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8746255/ |
| _version_ | 1798001998762606592 |
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| author | Basma Hassan Samir E. Abdelrahman Reem Bahgat Ibrahim Farag |
| author_facet | Basma Hassan Samir E. Abdelrahman Reem Bahgat Ibrahim Farag |
| author_sort | Basma Hassan |
| collection | DOAJ |
| description | Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in the state-of-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result. |
| first_indexed | 2024-04-11T11:45:11Z |
| format | Article |
| id | doaj.art-28e516f6122e464ab96d5b7759f86b13 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:45:11Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-28e516f6122e464ab96d5b7759f86b132022-12-22T04:25:35ZengIEEEIEEE Access2169-35362019-01-017854628548210.1109/ACCESS.2019.29250068746255UESTS: An Unsupervised Ensemble Semantic Textual Similarity MethodBasma Hassan0https://orcid.org/0000-0002-5040-2935Samir E. Abdelrahman1https://orcid.org/0000-0001-6396-3259Reem Bahgat2Ibrahim Farag3Computer Science Department, Faculty of Computers and Information, Fayoum University, Fayoum, EgyptComputer Science Department, Faculty of Computers and Information, Cairo University, Giza, EgyptComputer Science Department, Faculty of Computers and Information, Cairo University, Giza, EgyptComputer Science Department, Faculty of Computers and Information, Cairo University, Giza, EgyptSemantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in the state-of-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.https://ieeexplore.ieee.org/document/8746255/Semantic textual similarityword alignmentstring kernelBabelNetSemEvaltext processing |
| spellingShingle | Basma Hassan Samir E. Abdelrahman Reem Bahgat Ibrahim Farag UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method IEEE Access Semantic textual similarity word alignment string kernel BabelNet SemEval text processing |
| title | UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method |
| title_full | UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method |
| title_fullStr | UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method |
| title_full_unstemmed | UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method |
| title_short | UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method |
| title_sort | uests an unsupervised ensemble semantic textual similarity method |
| topic | Semantic textual similarity word alignment string kernel BabelNet SemEval text processing |
| url | https://ieeexplore.ieee.org/document/8746255/ |
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