A Neural Local Coherence Model
<p dir="ltr">We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the powe...
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2017
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| _version_ | 1864513557082669056 |
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| author | Dat Tien Nguyen (19720057) |
| author2 | Shafiq Joty (4576078) |
| author2_role | author |
| author_facet | Dat Tien Nguyen (19720057) Shafiq Joty (4576078) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dat Tien Nguyen (19720057) Shafiq Joty (4576078) |
| dc.date.none.fl_str_mv | 2017-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.18653/v1/p17-1121 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/conference_contribution/A_Neural_Local_Coherence_Model/27082693 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Local Coherence Model Convolutional Neural Network (CNN) Entity Grid Representation Entity-Specific Features Distributed Representation Pairwise Ranking Method End-to-End Training |
| dc.title.none.fl_str_mv | A Neural Local Coherence Model |
| dc.type.none.fl_str_mv | Text Conference contribution info:eu-repo/semantics/publishedVersion text conference object |
| description | <p dir="ltr">We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p17-1121" target="_blank">https://dx.doi.org/10.18653/v1/p17-1121</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 518–523 Vancouver, Canada, July 30 - August 4, 2017</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_560c53ac99accc431e2237656c53bacc |
| identifier_str_mv | 10.18653/v1/p17-1121 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27082693 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Neural Local Coherence ModelDat Tien Nguyen (19720057)Shafiq Joty (4576078)Information and computing sciencesArtificial intelligenceMachine learningLanguage, communication and cultureLinguisticsLocal Coherence ModelConvolutional Neural Network (CNN)Entity Grid RepresentationEntity-Specific FeaturesDistributed RepresentationPairwise Ranking MethodEnd-to-End Training<p dir="ltr">We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p17-1121" target="_blank">https://dx.doi.org/10.18653/v1/p17-1121</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 518–523 Vancouver, Canada, July 30 - August 4, 2017</p>2017-01-01T00:00:00ZTextConference contributioninfo:eu-repo/semantics/publishedVersiontextconference object10.18653/v1/p17-1121https://figshare.com/articles/conference_contribution/A_Neural_Local_Coherence_Model/27082693CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270826932017-01-01T00:00:00Z |
| spellingShingle | A Neural Local Coherence Model Dat Tien Nguyen (19720057) Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Local Coherence Model Convolutional Neural Network (CNN) Entity Grid Representation Entity-Specific Features Distributed Representation Pairwise Ranking Method End-to-End Training |
| status_str | publishedVersion |
| title | A Neural Local Coherence Model |
| title_full | A Neural Local Coherence Model |
| title_fullStr | A Neural Local Coherence Model |
| title_full_unstemmed | A Neural Local Coherence Model |
| title_short | A Neural Local Coherence Model |
| title_sort | A Neural Local Coherence Model |
| topic | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Local Coherence Model Convolutional Neural Network (CNN) Entity Grid Representation Entity-Specific Features Distributed Representation Pairwise Ranking Method End-to-End Training |