Extracting core answers using the grey wolf optimizer in community question answering

Abstract

A rapidly increasing number of users in community question answering has led to an explosive growth of answers. Thus, it is becoming increasingly more difficult to browse all the answers. Choosing a subset of answers arbitrarily will likely lead to cognitive bias and even poor decisions. Reading a set of core answers that can cover most topics of all the answers is a novel method to overcome information overload and facilitates information retrieval. In this paper, the method named AnsExt for extracting core answers using a kind of swarm intelligence algorithm, named the grey wolf optimizer (GWO), is proposed. First, answers are modeled with the biterm topic model, which fits both short and long texts. Then, factors including quality, coverage, redundancy and number in extracting core answers are defined. Two scenarios are modeled with the requirements of quality, coverage and redundancy: extracting the least number of answers and extracting a predefined number of answers. To extract the minimum number of core questions, a binary GWO is used to resolve the single-objective optimal model. The binary multi-objective GWO is constructed to resolve the optimal model, which is used to extract a predefined number of core answers. Extensive experiments are conducted on real datasets. The results show that the proposed method is feasible and performs well.