Fuzzy-Based Answer Ranking in Question Answering Communities

Abstract

Owing to the vast amount of information readily available on the World Wide Web, there has been a significant increase in the number of online question answering (QA) systems. A branch of QA systems that has seen such remarkable growth is the community-based question answering (CQA) systems. In this paper, the authors propose a method that is proactive enough to provide answers to questions and additionally offers word definitions, with the aim of reducing the time lag that results from askers having to wait for answers to a question from various users. Additionally, it designs a method to evaluate and predict the quality of an answer in a CQA setting, based on experts' rating. It uses fuzzy logic to aggregate the ratings and provide ranked answers in return. Experimental results with computing-related datasets from Yahoo! Answers demonstrate the effectiveness of the proposed techniques.

Development of Answer Validation System Using Responders’ Attributes and Crowd Ranking

“…Several studies have been carried out on how to make better the quality of the answers provided by QA system, focusing on textual entailment, question type analysis, answer ranking by the crowd workers and domain experts and personal and community features (past history) of the answerer to determine the quality of the answers (Ríos-Gaona et al, 2012;Su et al, 2007;Ishikawa et al, 2011;Ojokoh & Ayokunle, 2012;Anderson et al, 2012;Schofield & Thielscher, 2019).…”