Short Text Semantic Similarity Measurement Approach Based on Semantic Network
Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calcula...
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2022-12-01
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| Series: | Baghdad Science Journal |
| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/7255 |
| _version_ | 1811178519213899776 |
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| author | Naamah Hussien Hameed Adel M. Alimi Ahmed T. Sadiq |
| author_facet | Naamah Hussien Hameed Adel M. Alimi Ahmed T. Sadiq |
| author_sort | Naamah Hussien Hameed |
| collection | DOAJ |
| description |
Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that represents the relationship between the compared texts and extracts the degree of similarity between them. Representing a text as a semantic network is the best knowledge representation that comes close to the human mind's understanding of the texts, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. The network representation is a visual representation of knowledge objects, their qualities, and their relationships. WordNet lexical database has been used as a knowledge-based source while the GloVe pre-trained word embedding vectors have been used as a corpus-based source. The proposed method was tested using three different datasets, DSCS, SICK, and MOHLER datasets. A good result has been obtained in terms of RMSE and MAE.
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| first_indexed | 2024-04-11T06:19:47Z |
| format | Article |
| id | doaj.art-81ac293f56bc4397aa203c2bfbcab1c3 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-04-11T06:19:47Z |
| publishDate | 2022-12-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-81ac293f56bc4397aa203c2bfbcab1c32022-12-22T04:40:48ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862022-12-01196(Suppl.)10.21123/bsj.2022.7255 Short Text Semantic Similarity Measurement Approach Based on Semantic NetworkNaamah Hussien Hameed0Adel M. Alimi1Ahmed T. Sadiq2Computer Science Department, University of Technology, Baghdad, Iraq.1Computer Science Department, University of Technology, Baghdad, Iraq.Computer Science Department, University of Technology, Baghdad, Iraq. Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that represents the relationship between the compared texts and extracts the degree of similarity between them. Representing a text as a semantic network is the best knowledge representation that comes close to the human mind's understanding of the texts, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. The network representation is a visual representation of knowledge objects, their qualities, and their relationships. WordNet lexical database has been used as a knowledge-based source while the GloVe pre-trained word embedding vectors have been used as a corpus-based source. The proposed method was tested using three different datasets, DSCS, SICK, and MOHLER datasets. A good result has been obtained in terms of RMSE and MAE. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/7255 |
| spellingShingle | Naamah Hussien Hameed Adel M. Alimi Ahmed T. Sadiq Short Text Semantic Similarity Measurement Approach Based on Semantic Network Baghdad Science Journal |
| title | Short Text Semantic Similarity Measurement Approach Based on Semantic Network |
| title_full | Short Text Semantic Similarity Measurement Approach Based on Semantic Network |
| title_fullStr | Short Text Semantic Similarity Measurement Approach Based on Semantic Network |
| title_full_unstemmed | Short Text Semantic Similarity Measurement Approach Based on Semantic Network |
| title_short | Short Text Semantic Similarity Measurement Approach Based on Semantic Network |
| title_sort | short text semantic similarity measurement approach based on semantic network |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/7255 |
| work_keys_str_mv | AT naamahhussienhameed shorttextsemanticsimilaritymeasurementapproachbasedonsemanticnetwork AT adelmalimi shorttextsemanticsimilaritymeasurementapproachbasedonsemanticnetwork AT ahmedtsadiq shorttextsemanticsimilaritymeasurementapproachbasedonsemanticnetwork |