A survey on textual entailment based question answering
Question answering, an information retrieval system that seeks knowledge, is one of the classic applications in Natural Language Processing. A question answering system comprises numerous sets of subtasks. Some of the subtasks are Passage Retrieval, Answer Ranking, Question Similarity, Question Gene...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821003311 |
| _version_ | 1828091015805796352 |
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| author | Aarthi Paramasivam S. Jaya Nirmala |
| author_facet | Aarthi Paramasivam S. Jaya Nirmala |
| author_sort | Aarthi Paramasivam |
| collection | DOAJ |
| description | Question answering, an information retrieval system that seeks knowledge, is one of the classic applications in Natural Language Processing. A question answering system comprises numerous sets of subtasks. Some of the subtasks are Passage Retrieval, Answer Ranking, Question Similarity, Question Generation, Question Classification, Answer Selection, and Answer Validation. Numerous approaches have been experimented on in the question answering system to achieve accurate results. One such approach for the question answering system is Textual Entailment. Textual Entailment is a framework that captures significant semantic inference. Textual Entailment of two text fragments can be defined as the task of deciding whether the meaning of one text fragment can be inferred from another text fragment. This survey discusses how and why Textual Entailment is applied to various subtasks in question answering. |
| first_indexed | 2024-04-11T06:05:21Z |
| format | Article |
| id | doaj.art-92189b313e544cae90d2f1c90ebceee9 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-11T06:05:21Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-92189b313e544cae90d2f1c90ebceee92022-12-22T04:41:30ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-11-01341096449653A survey on textual entailment based question answeringAarthi Paramasivam0S. Jaya Nirmala1Corresponding author at: National Institute of Technology, Tiruchirappalli - 620015, Tamil Nadu, India.; Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, IndiaQuestion answering, an information retrieval system that seeks knowledge, is one of the classic applications in Natural Language Processing. A question answering system comprises numerous sets of subtasks. Some of the subtasks are Passage Retrieval, Answer Ranking, Question Similarity, Question Generation, Question Classification, Answer Selection, and Answer Validation. Numerous approaches have been experimented on in the question answering system to achieve accurate results. One such approach for the question answering system is Textual Entailment. Textual Entailment is a framework that captures significant semantic inference. Textual Entailment of two text fragments can be defined as the task of deciding whether the meaning of one text fragment can be inferred from another text fragment. This survey discusses how and why Textual Entailment is applied to various subtasks in question answering.http://www.sciencedirect.com/science/article/pii/S1319157821003311Natural Language ProcessingQuestion AnsweringTextual Entailment |
| spellingShingle | Aarthi Paramasivam S. Jaya Nirmala A survey on textual entailment based question answering Journal of King Saud University: Computer and Information Sciences Natural Language Processing Question Answering Textual Entailment |
| title | A survey on textual entailment based question answering |
| title_full | A survey on textual entailment based question answering |
| title_fullStr | A survey on textual entailment based question answering |
| title_full_unstemmed | A survey on textual entailment based question answering |
| title_short | A survey on textual entailment based question answering |
| title_sort | survey on textual entailment based question answering |
| topic | Natural Language Processing Question Answering Textual Entailment |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821003311 |
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