The Triplex Approach for Recognizing Semantic Relations from Noun Phrases, Appositions, and Adjectives
Discovering knowledge from textual sources and subsequently expanding the coverage of knowledge bases like DBpedia or Freebase currently requires either extensive manual work or carefully designed information extractors. Information extractors capture triples from textual sentences. Each triple consists of a subject, a predicate/property, and an object. Triples can be mediated via verbs, nouns, adjectives, and appositions. We propose Triplex, an information extractor that complements previous efforts, concentrating on noun-mediated triples related to nouns, adjectives, and appositions. Triplex automatically constructs templates expressing noun-mediated triples from a bootstrapping set. The bootstrapping set is constructed without manual intervention by creating templates that include syntactic, semantic, and lexical constraints. We report on an automatic evaluation method to examine the output of information extractors both with and without the Triplex approach. Our experimental study indicates that Triplex is a promising approach for extracting noun-mediated triples.
KeywordsOpen domain information extraction Relation extraction Noun-mediated relation triples Compound nouns Appositions
We would like to thank Matteo Palmonari for useful discussions. Cruz and Mirrezaei were partially supported by NSF Awards CCF-1331800, IIS-1213013, and IIS-1143926. Cruz was also supported by a Great Cities Institute scholarship. Martins was supported by the Portuguese FCT through the project grants EXCL/EEI-ESS/0257/2012 (DataStorm Research Line of Excellence) and PEst-OE/EEI/LA0021/2013 (INESC-ID’s Associate Laboratory multi-annual funding).
- 2.Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction for the web. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2670–2676 (2007)Google Scholar
- 3.Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)Google Scholar
- 4.Bronzi, M., Guo, Z., Mesquita, F., Barbosa, D., Merialdo, P.: Automatic evaluation of relation extraction systems on large-scale. In: NAACL-HLT Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX), pp. 19–24 (2012)Google Scholar
- 5.De Marneffe, M.C., Manning, C.D.: Stanford typed dependencies manual (2008). http://nlp.stanford.edu/software/dependencies_manual.pdf
- 6.Del Corro, L., Gemulla, R.: ClausIE: Clause-based open information extraction. In: International World Wide Web Conference (WWW), pp. 355–366 (2013)Google Scholar
- 7.Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 1535–1545 (2011)Google Scholar
- 8.Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: International Conference Research on Computational Linguistics (ROCLING), pp. 19–33 (1997)Google Scholar
- 9.Mausam, S.M., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 523–534 (2012)Google Scholar
- 10.Petrov, S., Das, D., McDonald, R.: A universal part-of-speech tagset. In: International Conference on Language Resources and Evaluation (LREC), pp. 2089–2096 (2011)Google Scholar
- 11.Singhal, A.: Introducing the Knowledge Graph: Things. Not Strings, Official Google Blog, May 2012Google Scholar
- 12.Wu, F., Weld, D.S.: Open information extraction using wikipedia. In: Annual Meeting of the Association for Computational Linguistics (ACL), pp. 118–127 (2010)Google Scholar
- 13.Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: ACM SIGMOD International Conference on Management of Data, pp. 481–492 (2012)Google Scholar
- 14.Xavier, C., Lima, V.: Boosting open information extraction with noun-based relations. In: International Conference on Language Resources and Evaluation (LREC), pp. 96–100 (2014)Google Scholar
- 15.Yahya, M., Whang, S.E., Gupta, R., Halevy, A.: Renoun: fact extraction for nominal attributes. In: Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 325–335 (2014)Google Scholar
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