In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit ecnica de Val encia: UH-PRHLT. Our system represents instances by using both lexical and semantic-based similarity measures between text pairs. Our semantic features include the use of distributed representations of words, knowledge graphs generated with the BabelNet multilingual semantic network, and the FrameNet lexical database. Experimental results outperform the random and Google search engine baselines in the three English subtasks. Our approach obtained the highest results of subtask B compared to the other task participants.
The previous edition of the SemEval CQA task included two English subtasks @cite_7 . The first one was focused on classifying answers as , , or with respect to one question. The second subtask answered a question with , , or based on the list of all answers. In addition, the first subtask was also available in Arabic. Several teams experimented with complex solutions that included meta-learning, external resources, and linguistic features such as syntactic relations and distributed word representations. Similarly to our work, the highest performing approach employed a combination of lexical and semantic-based similarity measures @cite_13 . Its semantic features included the use of probabilistic topic models, translation obfuscation-based alignments, and pre-computed distributed representations of words both generated with the word2vec https: code.google.com archive p word2vec and GloVe http: nlp.stanford.edu projects glove toolkits. Their lexical features included BOW, word alignments, and noun matching. They employed a regression model for classification. Another interesting approach, , included textual features --- word lengths and punctuation --- in addition to syntactical-based features --- Part-of-Speech (PoS) tags.
@cite_13 @cite_7
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In this paper, we describe our system for SemEval-2015 Task 3: Answer Selection in Community Question Answering. In this task, the systems are required to identify the good or potentially good answers from the answer thread in Community Question Answering collections. Our system combines 16 features belong to 5 groups to predict answer quality. Our final model achieves the best result in subtask A for English, both in accuracy and F1score. Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as good, bad, or potentially relevant with respect to the question, and (b) answering a YES NO question with yes, no, or unsure, based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.