Manta ray optimized deep contextualized bi-directional long short-term memory based adaptive galactic swarm optimization for complex question answering

Abstract

Complex question answering (CQA) is used for human knowledge answering and community questions answering. CQA system is essential to overcome the complexities present in the question answering system. The existing techniques ignores the queries structure and resulting a significant number of noisy queries. The complex queries, distributed knowledge, composite approaches, templates, and ambiguity are the common challenges faced by the CQA. To solve these issues, this paper presents a new manta ray foraging optimized deep contextualized bidirectional long-short term memory based adaptive galactic swarm optimization (MDCBiLSTMAGSO) for CQA. At first, the given input question is preprocessed and the similarity assessment is performed to eliminate the misclassification. Afterwards, the attained keywords are mapped into applicant results to improve the answer selection. Next, a new similarity approach named InfoSelectivity is introduced for semantic similarity evaluation based on the closeness among elements. Then, the relevant answers are classified through the MDCBiLSTM and optimized by a new manta ray foraging optimization (MRFO). Finally, adaptive galactic swarm optimization (AGSO) resultant is the best output. The proposed scheme is implemented on the JAVA platform and the outputs of designed approach achieved the better results when compared with the existing approaches in average accuracy (98.2%).