With the rapid development of knowledge base, question answering based on knowledge base has become a hot research issue. In this paper, we focus on answering single-relation 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, out-degree. Experimental results show that context information can improve the result of simple question answering.
The work of answering single-relation factoid questions was first proposed by @cite_3 . In this work, they employ Memory Networks and introduce a new dataset SimpleQuestions, which was built on Freebase.
@cite_3
580074167
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct evidence given a question, which can be difficult in large-scale conditions. To this end, we introduce a new dataset of 100k questions that we use in conjunction with existing benchmarks. We conduct our study within the framework of Memory Networks (, 2015) because this perspective allows us to eventually scale up to more complex reasoning, and show that Memory Networks can be successfully trained to achieve excellent performance.