该文提出了一种基于查询路径排序的知识库问答系统。为了将简单问题与复杂的多约束问题统一处理,同时提高系统的准确性,该系统采用基于LambdaRank算法构建的排序模型,对查询路径按照与问题的相关度大小进行排序,选择与问题相关度最高的路径用于抽取答案。同时,该系统还应用了一种融合方法以提高实体识别的准确性。该文所构建的系统在CCKS2019 KBQA任务与CCKS2020 KBQA任务上均取得了较好的效果。
Abstract
We proposed a new Knowledge Base Question Answering System based on the technology of query path ranking in this paper. The system is able to handle both simple and complex multi-constraint questions. In order to improve the performance of the system, we use Lambda Rank algorithm to sort candidate query paths according to their correlation degree with a question. The candidate path with the highest correlation degree with a question is chosen and used to extract answers. Moreover, the system also adopted a kind of novel fusion method which improved the accuracy of the entity recognition problem. The system has achieved promising results in both CCKS2019 and CCKS2020 KBQA tasks.
关键词
知识库 /
问答系统 /
排序 /
多约束
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Key words
knowledge base /
Question Answering /
rank /
multi-constraint
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参考文献
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[4] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018.
[5] Joshi M, Chen D, Liu Y, et al. SpanBERT: Improving pre-training by representing and predicting spans[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 64-77.
[6] Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using siamese bert-networks[J]. arXiv preprint arXiv:1908.10084, 2019.
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脚注
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基金
传播内容认知国家重点实验室课题(A12002)
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