With the rapid development of knowledge bases (KBs), question answering (QA) based on KBs has become a hot research issue. The KB-QA technology can be divided into two technical routes: (1) symbol based representations, such as traditional semantic parsing, and (2) distribution based embedding. With the emergence of deep learning, the development of NLP has greatly promoted. The effect of KB-QA can be improved by combining deep learning with the above two technical routes respectively. In this paper, the impact of the second route (i.e., Distribution Embedding) combined with deep learning is mainly discussed. This route can be divided into pipeline frameworks and end-to-end frameworks. For comprehensive analysis, two frameworks (i.e., a pipeline framework, an end-to-end framework) are proposed to focus on answering single-relation factoid questions. In both of two frameworks, the effect of context information on the quality of QA is studied, such as the entity’s notable type, out-degree. In the pipeline framework, it includes two cascaded steps: entity detection and relation detection. In this framework, multiple modules need to be built, and corresponding training data sets must be constructed for them respectively. The entire process of the pipleine framework is complex, costly and has the problem of error propagation. In the end-to-end framework, the two subtasks of entity detection and relation detection are merged together, and then combined into one framework. Questions, entities and relations are mapped into the same semantic space through the encoding of the recurrent neural network. Moreover, the question-entity similarity and the question-relation similarity are calculated, so that the candidate answers can be sorted and selected. Moreover, character-level(char-level) encoding and self-attention mechanisms are combined 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, the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy.

随着知识库（KB）的快速发展，基于知识库的问答（QA）已成为研究的热点。KB-QA技术可以分为两种技术路线：（1）基于符号的表示形式，例如传统的语义解析；以及（2）基于分布的嵌入。随着深度学习的兴起，NLP的发展得到了极大的促进。通过将深度学习分别与以上两种技术路线相结合，可以提高KB-QA的效果。本文主要讨论第二种方法（即分布嵌入）与深度学习相结合的影响。该路线可以分为管道框架和端到端框架。为了进行全面分析，有两个框架（即管道框架，一个端到端的框架）被提出来专注于回答单一关系的类事实问题。在这两个框架中，研究了上下文信息对质量保证质量的影响，例如实体的显着类型，外向度。在管道框架中，它包括两个层叠的步骤：实体检测和关系检测。在这个框架中，需要构建多个模块，并且必须分别为它们构建相应的训练数据集。pipleine框架的整个过程很复杂，成本很高，并且存在错误传播的问题。在端到端框架中，将实体检测和关系检测的两个子任务合并在一起，然后组合为一个框架。通过递归神经网络的编码，问题，实体和关系被映射到相同的语义空间中。此外，计算问题-实体相似度和问题-关系相似度，以便可以对候选答案进行分类和选择。此外，字符级（字符级）编码和自我注意机制通过权重共享和多任务策略相结合，以提高质量检查的准确性。实验结果表明，无论是流水线框架还是端到端框架，上下文信息都可以通过简单的QA获得更好的结果。此外，端到端框架在准确性方面也能与最新方法相媲美。字符级（字符级）编码和自我注意机制通过权重共享和多任务策略相结合，以提高质量检查的准确性。实验结果表明，无论是流水线框架还是端到端框架，上下文信息都可以通过简单的QA获得更好的结果。此外，端到端框架在准确性方面也能与最新方法相媲美。字符级（字符级）编码和自我注意机制通过权重共享和多任务策略相结合，以提高质量检查的准确性。实验结果表明，无论是流水线框架还是端到端框架，上下文信息都可以通过简单的QA获得更好的结果。此外，端到端框架在准确性方面也能与最新方法相媲美。