Title: Unsupervised deep semantic and logical analysis for identification of solution posts from community answers

Authors: Niraj Kumar; Kannan Srinathan; Vasudeva Varma

Addresses: Department – Center for Security Theory and Algorithmic Research, IIIT-Hyderabad, Hyderabad-500032, India ' Department – Center for Security Theory and Algorithmic Research, IIIT-Hyderabad, Hyderabad-500032, India ' Department – Search and Information Extraction Lab 'SIEL', IIIT-Hyderabad, Hyderabad-500032, India

Abstract: 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/inappropriate 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 time consuming task. To solve these issues, we concentrate on deep semantic and logical relation between terms. For this, we introduce a novel semantic correlation graph to represent the text. The proposed representation helps us in the 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.

Keywords: discussion forums; normalised pointwise mutual information; NPMI; semantic correlation graphs; personalised pagerank algorithm; random walk; restart; RWR; community question answering; semantic relatedness; group average agglomerative clustering; solution posts; community answers; online communities; virtual communities; web based communities.

DOI: 10.1504/IJIDS.2016.076518

International Journal of Information and Decision Sciences, 2016 Vol.8 No.2, pp.153 - 178

Received: 28 Oct 2014
Accepted: 02 Apr 2015
Published online: 11 May 2016 *

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