Abstract
Building system that could answer questions in natural language is one of the most important natural language processing applications. Recently, the raise of large-scale open-domain knowledge base provides a new possible approach. Some existing systems conduct question-answering relaying on hand-craft features and rules, other work try to extract features by popular neural networks. In this paper, we adopt recurrent neural network to understand questions and find out the corresponding answer entities from knowledge bases based on word embedding and knowledge bases embedding. Question-answer pairs are used to train our multi-step system. We evaluate our system on FREEBASE and WEBQUESTIONS. The experimental results show that our system achieves comparable performance compared with baseline method with a more straightforward structure.
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Chen, S., Wen, J., Zhang, R. (2016). GRU-RNN Based Question Answering Over Knowledge Base. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_8
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DOI: https://doi.org/10.1007/978-981-10-3168-7_8
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