With the rapid development of knowledge bases (KBs), question answering (QA) based on KBs has become a hot research issue. In this paper, we propose two frameworks (i.e., a pipeline framework, an end-to-end framework) to focus on answering single-relation factoid questions. In both of two frameworks, we study the effect of context information on the quality of QA, such as the entity's notable type, out-degree. In the pipeline framework, it includes two cascaded steps: entity detection and relation detection. In the end-to-end framework, we combine char-level encoding and self-attention mechanisms, using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition, we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.
With the rapid development of knowledge base, question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity's notable type, outdegree. Experimental results show that context information can improve the result of simple question answering.
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