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description Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 2016 Italy EnglishPublisher:ACM Press Funded by:EC | CogNetEC| CogNetAuthors: Kateryna Tymoshenko; Daniele Bonadiman; Alessandro Moschitti;Kateryna Tymoshenko; Daniele Bonadiman; Alessandro Moschitti;handle: 11572/169991
Recent initiatives in IR community have shown the importance of going beyond factoid Question Answering (QA) in order to design useful real-world applications. Questions asking for descriptions or explanations are much more difficult to be solved, e.g., the machine learning models cannot focus on specific answer words or their lexical type. Thus, researchers have started to explore powerful methods for feature engineering. Two of the most promising methods are convolution tree kernels (CTKs) and convolutional neural networks (CNNs) as they have been shown to obtain high performance in the task of answer sentence selection in factoid QA. In this paper, we design state-of-the-art models for non-factoid QA also carried out on noisy data. In particular, we study and compare models for comment selection in a community QA (cQA) scenario, where the majority of questions regard descriptions or explanations. To deal with such complex task, we incorporate relational information holding between questions and comments as well as domain-specific features into both convolutional models above. Our experiments on a cQA corpus show that both CTK and CNN achieve the state of the art, also according to a direct comparison with the results obtained by the best systems of the SemEval cQA challenge.
IRIS - Institutional... arrow_drop_down IRIS - Institutional Research Information System of the University of TrentoOther literature type . Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: ACM Copyright PoliciesIRIS - Institutional Research Information System of the University of TrentoPart of book or chapter of book . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/2983323.2983906&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 8 citations 8 popularity Average influence Average impulse Top 10% Powered by BIP!more_vert IRIS - Institutional... arrow_drop_down IRIS - Institutional Research Information System of the University of TrentoOther literature type . Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: ACM Copyright PoliciesIRIS - Institutional Research Information System of the University of TrentoPart of book or chapter of book . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/2983323.2983906&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
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description Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 2016 Italy EnglishPublisher:ACM Press Funded by:EC | CogNetEC| CogNetAuthors: Kateryna Tymoshenko; Daniele Bonadiman; Alessandro Moschitti;Kateryna Tymoshenko; Daniele Bonadiman; Alessandro Moschitti;handle: 11572/169991
Recent initiatives in IR community have shown the importance of going beyond factoid Question Answering (QA) in order to design useful real-world applications. Questions asking for descriptions or explanations are much more difficult to be solved, e.g., the machine learning models cannot focus on specific answer words or their lexical type. Thus, researchers have started to explore powerful methods for feature engineering. Two of the most promising methods are convolution tree kernels (CTKs) and convolutional neural networks (CNNs) as they have been shown to obtain high performance in the task of answer sentence selection in factoid QA. In this paper, we design state-of-the-art models for non-factoid QA also carried out on noisy data. In particular, we study and compare models for comment selection in a community QA (cQA) scenario, where the majority of questions regard descriptions or explanations. To deal with such complex task, we incorporate relational information holding between questions and comments as well as domain-specific features into both convolutional models above. Our experiments on a cQA corpus show that both CTK and CNN achieve the state of the art, also according to a direct comparison with the results obtained by the best systems of the SemEval cQA challenge.
IRIS - Institutional... arrow_drop_down IRIS - Institutional Research Information System of the University of TrentoOther literature type . Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: ACM Copyright PoliciesIRIS - Institutional Research Information System of the University of TrentoPart of book or chapter of book . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/2983323.2983906&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 8 citations 8 popularity Average influence Average impulse Top 10% Powered by BIP!more_vert IRIS - Institutional... arrow_drop_down IRIS - Institutional Research Information System of the University of TrentoOther literature type . Part of book or chapter of book . Conference object . 2016 . Peer-reviewedLicense: ACM Copyright PoliciesIRIS - Institutional Research Information System of the University of TrentoPart of book or chapter of book . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/2983323.2983906&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu