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).
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