What Is the Longest River in the USA? Semantic Parsing for Aggregation Questions


  • Kun Xu Peking University
  • Sheng Zhang Peking University
  • Yansong Feng Peking University
  • Songfang Huang IBM China Research Lab
  • Dongyan Zhao Peking University




Answering natural language questions against structured knowledge bases (KB) has been attracting increasing attention in both IR and NLP communities. The task involves two main challenges: recognizing the questions' meanings, which are then grounded to a given KB. Targeting simple factoid questions, many existing open domain semantic parsers jointly solve these two subtasks, but are usually expensive in complexity and resources.In this paper, we propose a simple pipeline framework to efficiently answer more complicated questions, especially those implying aggregation operations, e.g., argmax, argmin.We first develop a transition-based parsing model to recognize the KB-independent meaning representation of the user's intention inherent in the question. Secondly, we apply a probabilistic model to map the meaning representation, including those aggregation functions, to a structured query.The experimental results showed that our method can better understand aggregation questions, outperforming the state-of-the-art methods on the Free917 dataset while still maintaining promising performance on a more challenging dataset, WebQuestions, without extra training.




How to Cite

Xu, K., Zhang, S., Feng, Y., Huang, S., & Zhao, D. (2015). What Is the Longest River in the USA? Semantic Parsing for Aggregation Questions. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9735