Author: ScienceDirect Publication: Neural Networks

Knowledge-based question answering has attracted a lot of attention in the research communities of natural language processing and information retrieval. However, existing studies do not adequately address the problem of answering complex questions which involve multiple entities and require extraction of facts from multiple relations. To address this issue, we propose a novel approach which learns the distributional representations of questions and candidate answers in a unified deep-learning framework based on directed-acyclic-graph-structured long short-term memory and memory networks. Specifically, the questions are encoded to match candidate directed acyclic subgraphs of the knowledge base, which are able to include information related to multiple entities and relations in the complex questions. The experimental results show that the proposed approach outperforms other methods on the widely used dataset SPADES, especially when dealing with complex questions with multiple entities.

Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary techniques for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) method to investigate the efficiency and effectiveness of the proposed framework. Experimental results were obtained from a benchmark dataset of thirty-five subjects and indicate that the extended CORRCA method used within the framework significantly outperforms the original CORCCA method. Accordingly, the proposed framework holds promise to enhance the performance of frequency recognition methods in SSVEP-based BCIs.