|Title||Question Answering via Phrasal Semantic Parsing|
|Affiliation||Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China.|
China Res Lab, Beijing, Peoples R China.
|Publisher||EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION|
|Citation||EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION.Univ Toulouse, Inst Rech Informatique Toulouse UMR 5505 CNRS, Toulouse, FRANCE,2015/1/1,9283(414-426).|
|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.|
|Appears in Collections:||信息科学技术学院|