publication . Conference object . 2016

Learning to Rank Non-Factoid Answers

Comment Selection in Web Forums
Tymoshenko, Kateryna; Bonadiman, Daniele; Moschitti, Alessandro;
Open Access English
  • Published: 24 Oct 2016
  • Publisher: ACM Press
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.
Persistent Identifiers
Fields of Science and Technology classification (FOS)
02 engineering and technology, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 020204 information systems
free text keywords: Feature engineering, Learning to rank, Tree kernel, Factoid, Information retrieval, Sentence, Question answering, Artificial intelligence, business.industry, business, Natural language processing, computer.software_genre, computer, Computer science, Convolutional neural network, SemEval
  • Digital Humanities and Cultural Heritage
Funded by
EC| CogNet
Building an Intelligent System of Insights and Action for 5G Network Management
  • Funder: European Commission (EC)
  • Project Code: 671625
  • Funding stream: H2020 | RIA
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Conference object . 2016
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