RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations
<p dir="ltr">We present a highly-flexible UIMA-based pipeline for developing structural kernelbased systems for relational learning from text, i.e., for generating training and test data for ranking, classifying short text pairs or measuring similarity between pieces of text. For exa...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2017
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513557092106240 |
|---|---|
| author | Kateryna Tymoshenko (19691680) |
| author2 | Alessandro Moschitti (19691683) Massimo Nicosia (19691686) Aliaksei Severyn (18370833) |
| author2_role | author author author |
| author_facet | Kateryna Tymoshenko (19691680) Alessandro Moschitti (19691683) Massimo Nicosia (19691686) Aliaksei Severyn (18370833) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kateryna Tymoshenko (19691680) Alessandro Moschitti (19691683) Massimo Nicosia (19691686) Aliaksei Severyn (18370833) |
| dc.date.none.fl_str_mv | 2017-07-30T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.18653/v1/p17-4014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/conference_contribution/RelTextRank_An_Open_Source_Framework_for_Building_Relational__Syntactic-Semantic_Text_Pair_Representations/27050725 |
| 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 UIMA Pipeline Structural kernel-based systems Relational learning Text processing Training data Test data |
| dc.title.none.fl_str_mv | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| dc.type.none.fl_str_mv | Text Conference contribution info:eu-repo/semantics/publishedVersion text conference object |
| description | <p dir="ltr">We present a highly-flexible UIMA-based pipeline for developing structural kernelbased systems for relational learning from text, i.e., for generating training and test data for ranking, classifying short text pairs or measuring similarity between pieces of text. For example, the proposed pipeline can represent an input question and answer sentence pairs as syntacticsemantic structures, enriching them with relational information, e.g., links between question class, focus and named entities, and serializes them as training and test files for the tree kernel-based reranking framework. The pipeline generates a number of dependency and shallow chunkbased representations shown to achieve competitive results in previous work. It also enables easy evaluation of the models thanks to cross-validation facilities.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of ACL 2017, System Demonstrations<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/p17-4014" target="_blank">https://dx.doi.org/10.18653/v1/p17-4014</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, Vancouver, Canada, July 30 - August 4, 2017<br></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f2b58056da5e2147d080bbc9207a5790 |
| identifier_str_mv | 10.18653/v1/p17-4014 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27050725 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair RepresentationsKateryna Tymoshenko (19691680)Alessandro Moschitti (19691683)Massimo Nicosia (19691686)Aliaksei Severyn (18370833)Information and computing sciencesArtificial intelligenceMachine learningLanguage, communication and cultureLinguisticsUIMAPipelineStructural kernel-based systemsRelational learningText processingTraining dataTest data<p dir="ltr">We present a highly-flexible UIMA-based pipeline for developing structural kernelbased systems for relational learning from text, i.e., for generating training and test data for ranking, classifying short text pairs or measuring similarity between pieces of text. For example, the proposed pipeline can represent an input question and answer sentence pairs as syntacticsemantic structures, enriching them with relational information, e.g., links between question class, focus and named entities, and serializes them as training and test files for the tree kernel-based reranking framework. The pipeline generates a number of dependency and shallow chunkbased representations shown to achieve competitive results in previous work. It also enables easy evaluation of the models thanks to cross-validation facilities.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of ACL 2017, System Demonstrations<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/p17-4014" target="_blank">https://dx.doi.org/10.18653/v1/p17-4014</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, Vancouver, Canada, July 30 - August 4, 2017<br></p>2017-07-30T06:00:00ZTextConference contributioninfo:eu-repo/semantics/publishedVersiontextconference object10.18653/v1/p17-4014https://figshare.com/articles/conference_contribution/RelTextRank_An_Open_Source_Framework_for_Building_Relational__Syntactic-Semantic_Text_Pair_Representations/27050725CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270507252017-07-30T06:00:00Z |
| spellingShingle | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations Kateryna Tymoshenko (19691680) Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics UIMA Pipeline Structural kernel-based systems Relational learning Text processing Training data Test data |
| status_str | publishedVersion |
| title | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| title_full | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| title_fullStr | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| title_full_unstemmed | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| title_short | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| title_sort | RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations |
| topic | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics UIMA Pipeline Structural kernel-based systems Relational learning Text processing Training data Test data |