MixKMeans: Clustering Question-Answer Archives
P Deepak · 2016
Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge.Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging.In this paper, we present a clustering method that exploits the two-part question-answer structure in QA datasets to improve clustering quality.Our method, MixKMeans, composes question and answer space similarities in a way that the space on which the match is higher is allowed to dominate.This construction is motivated by our observation that semantic similarity between question-answer data (QAs) could get localized in either space.We empirically evaluate our method on a variety of real-world labeled datasets.Our results indicate that our method significantly outperforms stateof-the-art clustering methods for the task of clustering question-answer archives.