Abstract
This paper describes a method for learning which relations are highly associated with a given seed relation such as marriage or working for a company. Relation instances taken from a large knowledge base are used as seeds for obtaining candidate sentences expressing the associated relations. Relations of interest are identified by parsing the sentences and extracting dependency graph fragments, which are then ranked to determine which of them are most closely associated with the seed relation. We call the sets of associated relations relation theories. The quality of the induced theories is evaluated using human judgements.
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.
References
Etzioni, O., Banko, M., Cafarella, M.J.: Machine Reading. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, AAAI 2006, Boston, Massachusetts, USA, pp. 1517–1519 (2006)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL 2009, Suntec, Singapore, vol. 2, pp. 1003–1011 (2009)
Kohlschütter, C., Fankhauser, P., Nejdl, W.: Boilerplate Detection using Shallow Text Features. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, New York, USA, pp. 441–450 (2010)
Fader, A., Soderland, S., Etzioni, O.: Identifying Relations for Open Information Extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Edinburgh, United Kingdom, pp. 1535–1545 (2011)
Mausam, S.M., Bart, R., Soderland, S., Etzioni, O.: Open Language Learning for Information Extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, Jeju Island, Korea, pp. 523–534 (2012)
Lin, D., Pantel, P.: DIRT – Discovery of Inference Rules from Text. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2001, Location Unknown, pp. 323–328 (2001)
Clark, S., Curran, J.R.: Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models. Computational Linguistics 33(4), 493–547 (2007)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)
Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 37–46 (1960)
Schoenmackers, S., Etzioni, O., Weld, D.S., Davis, J.: Learning first-order horn clauses from web text. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, Cambridge, Massachusetts, USA, pp. 1088–1098 (2010)
Berant, J., Dagan, I., Goldberger, J.: Global learning of typed entailment rules. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, Portland, Oregon, USA, vol. 1, pp. 610–619 (2011)
Liakata, M., Pulman, S.: Learning theories from text. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004, Geneva, Switzerland (2004)
Galárraga, L., Teflioudi, C., Suchanek, F., Hose, K.: AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases. In: Proceedings of the 22nd International World Wide Web Conference (WWW 2013), Rio do Janeiro, Brazil (2013)
Chambers, N., Jurafsky, D.: Unsupervised Learning of Narrative Schemas and their Participants. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL 2009, Suntec, Singapore, vol. 2, pp. 602–610 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bauer, S., Clark, S., Rimell, L., Graepel, T. (2014). Learning a Theory of Marriage (and Other Relations) from a Web Corpus. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-06028-6_62
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06027-9
Online ISBN: 978-3-319-06028-6
eBook Packages: Computer ScienceComputer Science (R0)