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Ranking community answers by modeling question-answer relationships via analogical reasoning
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In this paper we study whether a question and its answer can be related using analogical reasoning by using various kinds of textual occurrences in a question answering (QA) task. We argue that in a QA passage retrieval context, low cost language features can contribute some positive influence in the representation of the information need that also appears in other passages, which have some analogical features. We attempt to leverage this through query expansion and query stopwords exchange strategies among analogical question answer pairs, which are modeled by a Bayesian Analogical Reasoning framework. Our study by using ResPubliQA 2009 and 2010 dataset shows that the predicted analogical relation between question answer pairs can be used to maintain the information need of the QA passage retrieval task, but has a poor performance in determining the question type. Our best accuracy score was achieved by using‘bigram occurrences by using stemmer and TF-IDF weighting completed with named-entity' feature set for the query expansion approach, and ‘bigram occurrences by using stemmer and TF-IDF weighting' feature set for the stopwords exchanged approach.