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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fader, A., Soderland, S., Etzioni, O.: Identifying Relations for Open Information Extraction. In: EMNLP, pp. 1535–1545 (2011)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: WWW, pp. 697–706 (2007)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: A Nucleus for a Web of Open Data. In: Aberer, K., et al. (eds.) ASWC/ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: A collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic Parsing on Freebase from Question-Answer Pairs. In: EMNLP, pp. 1533–1544 (2013)
Cimiano, P., Lopez, V., Unger, C., Cabrio, E., Ngonga Ngomo, A.-C., Walter, S.: Multilingual Question Answering over Linked Data (QALD-3): Lab Overview. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 321–332. Springer, Heidelberg (2013)
Kwiatkowski, T., Choi, E., Artzi, Y., Zettlemoyer, L.S.: Scaling Semantic Parsers with On-the-Fly Ontology Matching. In: EMNLP, pp. 1545–1556 (2013)
Frank, A., Krieger, H.-U., Xu, F., Uszkoreit, H., Crysmann, B., Jörg, B., Schäfer, U.: Question answering from structured knowledge sources. J. Applied Logic, 20–48 (2007)
Yahya, M., Berberich, K., Elbassuoni, S., Ramanath, M., Tresp, V., Weikum, G.: Natural Language Questions for the Web of Data. In: EMNLP-CoNLL, pp. 379–390 (2012)
Unger, C., Bühmann, L., Lehmann, J., Ngonga Ngomo, A.-C., Gerber, D., Cimiano, P.: Template-based question answering over RDF data. In: WWW, pp. 639–648 (2012)
Krishnamurthy, J., Mitchell, T.: Weakly Supervised Training of Semantic Parsers. In: EMNLP-CoNLL, pp. 754–765 (2012)
Cai, Q., Yates, A.: Semantic Parsing Freebase: Towards Open-domain Semantic Parsing. In: SEM (2013)
Kwiatkowski, T., Choi, E., Artzi, Y., Zettlemoyer, L.S.: Scaling Semantic Parsers with On-the-Fly Ontology Matching. In: EMNLP, pp. 1545–1556 (2013)
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic Parsing on Freebase from Question-Answer Pairs. In: EMNLP, pp. 1533–1544 (2013)
Berant, J., Liang, P.: Semantic Parsing via Paraphrasing. In: ACL (2014)
Unger, C., Cimiano, P.: Pythia: Compositional Meaning Construction for Ontology-based Question Answering on the Semantic Web. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 153–160. Springer, Heidelberg (2011)
Kwiatkowski, T., Zettlemoyer, L.S., Goldwater, S., Steedman, M.: Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification. In: EMNLP, pp. 1223–1233 (2010)
Collins, M.: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. In: EMNLP (2002)
Sagae, K., Tsujii, J.: Shift-Reduce Dependency DAG Parsing. In: COLING, pp. 753–760 (2008)
Collins, M., Roark, B.: Incremental Parsing with the Perceptron Algorithm. In: ACL, pp. 111–118 (2004)
Yao, X., Van Durme, B.: Information Extraction over Structured Data: Question Answering with Freebase. In: Proceedings of ACL (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-662-45924-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45923-2
Online ISBN: 978-3-662-45924-9
eBook Packages: Computer ScienceComputer Science (R0)