CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 18–22, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task Helena G´omez-Adorno, Grigori Sidorov Center for Computing Research Instituto Polit´ecnico Nacional Av. Juan de Dios B´atiz C.P. 07738, Mexico City, Mexico helena.adorno@gmail.com sidorov@cic.ipn.mx Darnes Vilari˜no, David Pinto Faculty of Computer Science Benem´erita Universidad Aut´onoma de Puebla Av. San Claudio y 14 sur C.P. 72570, Puebla, Mexico darnes@cs.buap.mx dpinto@cs.buap.mx Abstract This paper describes our approach for the Community Question Answering Task, which was presented at the SemEval 2015. The sys- tem should read a given question and identify good, potentially relevant, and bad answers for that question. Our approach transforms the answers of the training set into a graph based representation for each answer class, which contains lexical, morphological, and syntactic features. The answers in the test set are also transformed into the graph based representa- tion individually. After this, different paths are traversed in the training and test sets in order to find relevant features of the graphs. As a result of this procedure, the system constructs several vectors of features: one for each tra- versed graph. Finally, a cosine similarity is calculated between the vectors in order to find the class that best matches a given answer. Our system was developed for the English lan- guage only, and it obtained an accuracy of 53.74 for subtask A and 44.0 for subtask B. 1 Introduction In this paper we present the experiments carried out as part of our participation in the SemEval-2015 Task 3 (Answer Selection in Community Question Answering). The Answer Selection in Commu- nity Question Answering task is proposed for the first time this year in the International Workshop on Semantic Evaluation (SemEval-2015). The task is based on an application scenario, which is related to textual entailment, semantic similarity and NL infer- ence. Community question answering (CQA) websites enable people to post questions and answers in var- ious domains. In this way, users can obtain specific answers to their questions, instead of searching in the large volume of information available in the web. However, it takes effort to go through all possible an- swers and select which one is the most accurate one for a specific question. The task proposes to auto- mate this process by predicting the quality of exist- ing answers with respect to a question. There are few works in the literature on evaluat- ing the quality of answers provided in CQA sites. Most of such works employ non-textual and tem- poral features in order to built classification models for predicting the best answer for a given question. In (Jeon et al., 2006), the authors extract 13 non- textual features from the Naver data set and build a maximum entropy classification model to predict the quality (three classes: Bad, Medium and Good) of a given answer. A similar approach is used in (Shah and Pomerantz, 2010), but extracting 21 fea- tures …

Content truncated to 500 words