Previous work on Automatic Paraphrase Iden- tification (PI) is mainly based on modeling text similarity between two sentences. In con- trast, we study methods for automatically de- tecting whether a text fragment only appear- ing in a sentence of the evaluated sentence pair is important or ancillary information with re- spect to the paraphrase identification task. En- gineering features for this new task is rather difficult, thus, we approach the problem by representing text with syntactic structures and applying tree kernels on them. The results show that the accuracy of our automatic An- cillary Text Classifier (ATC) is promising, i.e., 68.6%, and its output can be used to improve the state of the art in PI.

Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums / Timoshenko, Kateryna; Bonadiman, Daniele; Moschitti, Alessandro. - ELETTRONICO. - (2016), pp. 2049-2052. [10.1145/2983323.2983906]

Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums

Bonadiman, Daniele;Moschitti, Alessandro
2016

Abstract

Previous work on Automatic Paraphrase Iden- tification (PI) is mainly based on modeling text similarity between two sentences. In con- trast, we study methods for automatically de- tecting whether a text fragment only appear- ing in a sentence of the evaluated sentence pair is important or ancillary information with re- spect to the paraphrase identification task. En- gineering features for this new task is rather difficult, thus, we approach the problem by representing text with syntactic structures and applying tree kernels on them. The results show that the accuracy of our automatic An- cillary Text Classifier (ATC) is promising, i.e., 68.6%, and its output can be used to improve the state of the art in PI.
Proceedings of the 25th {ACM} International on Conference on Information and Knowledge Management, {CIKM}
New York NY, USA
ACM
Timoshenko, Kateryna; Bonadiman, Daniele; Moschitti, Alessandro
Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums / Timoshenko, Kateryna; Bonadiman, Daniele; Moschitti, Alessandro. - ELETTRONICO. - (2016), pp. 2049-2052. [10.1145/2983323.2983906]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/169991
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