A Deep Paraphrase Identification Model Interacting Semantics with Syntax
Paraphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model intera...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9757032 |
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| _version_ | 1849397863625261056 |
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| author | Leilei Kong Zhongyuan Han Yong Han Haoliang Qi |
| author_facet | Leilei Kong Zhongyuan Han Yong Han Haoliang Qi |
| author_sort | Leilei Kong |
| collection | DOAJ |
| description | Paraphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model interacting semantics with syntax (DPIM-ISS) for paraphrase identification. DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. Experiments are conducted on the corpus of Microsoft Research Paraphrase (MSRP), PAN 2010 corpus, and PAN 2012 corpus for paraphrase plagiarism detection. The experimental results demonstrate that DPIM-ISS outperforms the classical word-matching approaches, the syntax-similarity approaches, the convolution neural network-based models, and some deep paraphrase identification models. |
| format | Article |
| id | doaj-art-a7bc453cfb7f46a4b33c505249a69d17 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a7bc453cfb7f46a4b33c505249a69d172025-08-20T03:38:49ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/97570329757032A Deep Paraphrase Identification Model Interacting Semantics with SyntaxLeilei Kong0Zhongyuan Han1Yong Han2Haoliang Qi3School of Electronic Information Engineering, Foshan University, Foshan 528225, ChinaSchool of Electronic Information Engineering, Foshan University, Foshan 528225, ChinaSchool of Electronic Information Engineering, Foshan University, Foshan 528225, ChinaSchool of Electronic Information Engineering, Foshan University, Foshan 528225, ChinaParaphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model interacting semantics with syntax (DPIM-ISS) for paraphrase identification. DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. Experiments are conducted on the corpus of Microsoft Research Paraphrase (MSRP), PAN 2010 corpus, and PAN 2012 corpus for paraphrase plagiarism detection. The experimental results demonstrate that DPIM-ISS outperforms the classical word-matching approaches, the syntax-similarity approaches, the convolution neural network-based models, and some deep paraphrase identification models.http://dx.doi.org/10.1155/2020/9757032 |
| spellingShingle | Leilei Kong Zhongyuan Han Yong Han Haoliang Qi A Deep Paraphrase Identification Model Interacting Semantics with Syntax Complexity |
| title | A Deep Paraphrase Identification Model Interacting Semantics with Syntax |
| title_full | A Deep Paraphrase Identification Model Interacting Semantics with Syntax |
| title_fullStr | A Deep Paraphrase Identification Model Interacting Semantics with Syntax |
| title_full_unstemmed | A Deep Paraphrase Identification Model Interacting Semantics with Syntax |
| title_short | A Deep Paraphrase Identification Model Interacting Semantics with Syntax |
| title_sort | deep paraphrase identification model interacting semantics with syntax |
| url | http://dx.doi.org/10.1155/2020/9757032 |
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