Abstract
Question answering over linked data has emerged in the past years as an important topic of research in order to provide natural language access to a growing body of linked open data on the Web. In this paper we focus on analyzing the lexical gap that arises as a challenge for any such question answering system. The lexical gap refers to the mismatch between the vocabulary used in a user question and the vocabulary used in the relevant dataset. We implement a semantic parsing approach and evaluate it on the QALD-4 benchmark, showing that the performance of such an approach suffers from training data sparseness. Its performance can, however, be substantially improved if the right lexical knowledge is available. To show this, we model a set of lexical entries by hand to quantify the number of entries that would be needed. Further, we analyze if a state-of-the-art tool for inducing ontology lexica from corpora can derive these lexical entries automatically. We conclude that further research and investments are needed to derive such lexical knowledge automatically or semi-automatically.
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References
Artzi, Y., Zettlemoyer, L.: Bootstrapping semantic parsers from conversations. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 421–432. Association for Computational Linguistics (2011)
Artzi, Y., Zettlemoyer, L.: Weakly supervised learning of semantic parsers for mapping instructions to actions. TACL 1, 49–62 (2013)
Carpenter, B.: Type-Logical Semantics. MIT Press, Cambridge (1997)
Krishnamurthy, J., Mitchell, M.T.: Joint syntactic and semantic parsing with combinatory categorial grammar. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1188–1198 (2014)
Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., Steedman, M.: Inducing probabilistic CCG grammars from logical form with higher-order unification. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1223–1233. Association for Computational Linguistics (2010)
Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., Steedman, M.: Lexical generalization in CCG grammar induction for semantic parsing. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. pp. 1512–1523. Association for Computational Linguistics (2011)
Lopez, V., Unger, C., Cimiano, P., Motta, E.: Evaluating question answering over linked data. Web Semant. Sci. Serv. Agents World Wide Web 21, 3–13 (2013)
Lopez, V., Uren, V., Sabou, M., Motta, E.: Is Question Answering fit for the Semantic Web? A Survey. Semant. Web 2, 125–155 (2011)
Steedman, M.: Surface Structure and Interpretation. MIT Press, Cambridge (1996)
Steedman, M.: The Syntactic Process, vol. 35. MIT Press, Cambridge (2000)
Unger, C., Forascu, C., Lopez, V., Ngonga Ngomo, A.C., Cabrio, E., Cimiano, P., Walter, S.: Question Answering over Linked Data (QALD-4). In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) Working Notes for CLEF 2014 Conference (2014)
Walter, S., Unger, C., Cimiano, P.: ATOLL - a framework for the automatic induction of ontology lexica. Data Knowl. Eng. 94, 148–162 (2014)
Walter, S., Unger, C., Cimiano, P.: M-ATOLL: a framework for the lexicalization of ontologies in multiple languages. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 472–486. Springer, Heidelberg (2014)
Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. arXiv preprint (2005). arXiv:1207.1420
Zettlemoyer, L.S., Collins, M.: Online learning of relaxed CCG grammars for parsing to logical form. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL-2007. Citeseer (2007)
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Hakimov, S., Unger, C., Walter, S., Cimiano, P. (2015). Applying Semantic Parsing to Question Answering Over Linked Data: Addressing the Lexical Gap. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_8
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DOI: https://doi.org/10.1007/978-3-319-19581-0_8
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