IIIT Hyderabad Publications
Unsupervised deep semantic and logical analysis for identification of solution posts from community answers
Authors: Niraj Kumar,Kannan Srinathan,Vasudeva Varma
Journal: Int. J. Information and Decision Sciences (link)
Volume Number: 2
Report no: IIIT/TR/2016/6
These days’ discussion forums provide dependable solutions to the problems related to multiple domains and areas. However, due to the presence of huge amount of less-informative/inappr opriate posts, the identification of the appropriate problem-solution pairs has become a challenging task. The emergence of a variety of topics, domains and areas has made the task of manual labelling of the problem solution-post pairs a very costly and timeconsuming task. To solve these issues, we concentrate on deep semantic andlogical relation between terms. For this, we introduce a novel semantic correlation graph to represent the text. The proposed representation helps us inthe identification of topical and semantic relation between terms at a fine grain level. Next, we apply the improved version of personalised pagerank using random walk with restarts. The main aim is to improve the rank score of terms having direct or indirect relation with terms in the given question. Finally, we introduce the use of the node overlapping version of GAAC to find the actual span of answer text. Our experimental results show that the devised system performs better than the existing unsupervised systems.
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