Abstract
Understanding natural language questions and converting them into structured queries have been considered as a crucial way to help users access large scale structured knowledge bases. However, the task usually involves two main challenges: recognizing users’ query intention and mapping the involved semantic items against a given knowledge base (KB). In this paper, we propose an efficient pipeline framework to model a user’s query intention as a phrase level dependency DAG which is then instantiated regarding a specific KB to construct the final structured query. Our model benefits from the efficiency of linear structured prediction models and the separation of KB-independent and KB-related modelings. We evaluate our model on two datasets, and the experimental results showed that our method outperforms the state-of-the-art methods on the Free917 dataset, and, with limited training data from Free917, our model can smoothly adapt to new challenging dataset, WebQuestion, without extra training efforts while maintaining promising performances.
This work was supported by the National High Technology R&D Program of China (Grant No. 2012AA011101, 2014AA015102), National Natural Science Foundation of China (Grant No. 61272344, 61202233, 61370055) and the joint project with IBM Research.
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Xu, K., Zhang, S., Feng, Y., Zhao, D. (2014). Answering Natural Language Questions via Phrasal Semantic Parsing. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_30
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DOI: https://doi.org/10.1007/978-3-662-45924-9_30
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