Answer selection using Word Alignment based on Part of Speech Tagging in community question answering

Abstract

This paper explain about answer selection using word alignment based on POS tagging in Community Question-Answering (CQA). This online community allowed the user to ask and reply related to the question problems which has no restrictions. This causes inappropriate comments with the question problems proposed before. To solve these problems, combining lexical and semantic features has been developed with result conclude that the approach more adequate for similarity task rather than question answering. According to the previous research, there is several problems that can be enhanced. First, vector representation counts exactly matched words, so it does not effective to cover other words that have relatedness between two pairing words. Second, noun overlap for similarity measure in pairing words can’t define that the two words are similar. So, it must be define that the pairing POS tag is the same meaning or relatedness. In this study, unsupervised lexical and semantic similarity method employed with different approach from previous method in verbatim and contextual similarities. The data was taken from SemEval 2017 competition which focus on Question-Answer Similarity task. The experiment result for precision (Mean Average Precision) score shows the improvement from 0.674 to 0.6845, 1.03 % higher than previous research in CQA. This improvement comes from lexical similarity, which is not just from noun pattern but also taken from verb pattern. Furthermore, semantic similarity has an important role in determining which words that have same pattern and meaning to define relevancy between them.