SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings
Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity b...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6627434 |
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| author | Rawan N. Al-Matham Hend S. Al-Khalifa |
| author_facet | Rawan N. Al-Matham Hend S. Al-Khalifa |
| author_sort | Rawan N. Al-Matham |
| collection | DOAJ |
| description | Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages. |
| format | Article |
| id | doaj-art-087d3c51dfb54b3f85bd0c8fe06f8676 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-087d3c51dfb54b3f85bd0c8fe06f86762025-02-03T01:04:12ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66274346627434SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word EmbeddingsRawan N. Al-Matham0Hend S. Al-Khalifa1Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 12371, Riyadh, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, King Saud University, P.O. Box 12371, Riyadh, Saudi ArabiaAutomatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages.http://dx.doi.org/10.1155/2021/6627434 |
| spellingShingle | Rawan N. Al-Matham Hend S. Al-Khalifa SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings Complexity |
| title | SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings |
| title_full | SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings |
| title_fullStr | SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings |
| title_full_unstemmed | SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings |
| title_short | SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings |
| title_sort | synoextractor a novel pipeline for arabic synonym extraction using word2vec word embeddings |
| url | http://dx.doi.org/10.1155/2021/6627434 |
| work_keys_str_mv | AT rawannalmatham synoextractoranovelpipelineforarabicsynonymextractionusingword2vecwordembeddings AT hendsalkhalifa synoextractoranovelpipelineforarabicsynonymextractionusingword2vecwordembeddings |