Abstract
Aiming at open domain question answering system evaluation task in the fourth CCF Natural Language Processing and Chinese Computing Conference (NLPCC2015), a solution of automatic question answering which can answer natural language questions is proposed. Firstly, SPE (Subject Predicate Extraction) algorithm is presented to find answers from the knowledge base, and then WKE (Web Knowledge Extraction) algorithm is used to extract answers from search engine query result. Experimental data provided in the evaluation task includes the knowledge base and questions in natural language. The evaluation result shows that MRR is 0.5670, accuracy is 0.5700, and average F1 is 0.5240, and indicates the proposed method is feasible in open domain question answering system.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Mooney, R.J.: Learning for semantic parsing. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 311–324. Springer, Heidelberg (2007)
Filman, R.E., Sangam, P.: Searching the Internet. IEEE Internet Computing 2(4), 21–23 (1998)
Jeon, J., Croft, W.B., Lee, J.H.: Finding simliar question in large question and answer archives. In: Proceedings of the ACM Fourteen Conference on Inference and Knowledge Management, pp. 84−90 (2005)
Zettlemonyer, L.S., Collins, M.: Learning to map sentence to logical form: Structured classification with probabilistic catagorical grammars. In: Proceedings of the 21th Conference on Uncertainty in Artificial Intelligence, pp. 658−666 (2005)
Wong, Y.W., Mooney, R.J.: Learning for semantic parsing with statistical machine translation. In: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pp. 439−446 (2006)
Wong, Y.W., Mooney, R.J.: Generation by inverting a semantic parser that uses statistical machine translation. In: Proceeding of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Rochester, pp. 172−179 (2007)
Zettlemoyer, L., Collions, M.: Online learning of relaxed CCG grammars for parsin to logical form. In: Proceeddings of ENNLP, pp. 678−687 (2007)
Kwiatkowski, T., Luke, S., ZettleMoyer, S.G.: Lexical generalization in CCG grammer induction for semantic parsing. In: Proceeddings of ENNLP, pp. 1512−1523 (2011)
Kwiatkowski, T., Luke, S., ZettleMoyer, S.G.: Inducing probabilistic CCG grammar form with higherorder unification. In: Proceeddings of EMNLP, pp. 1223−1233(2010)
Freebase (2015). http://www.freebase.com
Poon, H.F., Domingos, P.: Unsupervised semantic parsing. In: Proceeddings of EMNLP, pp. 1−10 (2009)
Yahya, M., Berberich, K., Elbassuoni, S.: Natural language question for the web of data. In: Proceedings of EMNLP, pp. 379−390 (2012)
Berant, J., Chou, A., Roy, F., et al.: Freebase QA: Information extraction or semantic parsing. In: The 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1511−1527 (2014)
Berant, J., Chou, A., Roy, F., et al.: Semantic parsing on freebase from question-answer pairs. In: The 2013 Conference on Empirical Methods on Natural Language Processing, pp. 153−1544 (2013)
Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: The 52nd Annual Meeting of the Association for Computational Linguistics, pp. 479-485 (2014)
Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: The 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1535−1545 (2014)
Yao, X., Durme, B.: Information extraction over structured data: question answering with freebase. In: The 52nd Annual Meeting of the Association for Computational Linguistics, pp. 753−770 (2014)
Fader, A., Luke, Z., Oren, E.: Open question answering over curated and extracted knowledge bases. In: Proceedings of the Conference on Knowledge Discovery and Data Mining (KDD), pp. 1256−1265 (2014)
Kushman, N., Artzi, Y., Luke, Z., et al.: Learning to automatically solve algebra word problems. In: The 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1042−1061 (2014)
Bao, J., Duan, N., Zhou, M., et al.: Knowledge-based question answering as machine translation. In: The 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1272−1294 (2014)
Cristina, E.B., Pere, R., Comas.: Full machine translation for factoid question answering. In: Proceedings of EACL, pp. 20−29. ACL, Stroudsburg (2012)
Wei, C.Y., Zhan, Q., Fan, X.Z., et al.: Event Information Enhanced Question Semantic Representation for Chinese Question Answering System. Journal of Chinese Information Processing 1(29), 147–154 (2015)
SWJTU Chinese Word Segmentation System. http://ics.swjtu.edu.cn
Cho, H.C., Okazaki, N., Miwa, M., et al.: Named Entity Recognition with Multiple Segment Representation. Information Processing Management 49(4), 954–965 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ye, Z., Jia, Z., Yang, Y., Huang, J., Yin, H. (2015). Research on Open Domain Question Answering System. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-25207-0_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25206-3
Online ISBN: 978-3-319-25207-0
eBook Packages: Computer ScienceComputer Science (R0)