Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis

Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis Claudio Delli Bovi, Luca Telesca and Roberto Navigli Department of Computer Science Sapienza University of Rome {dellibovi,navigli}@di.uniroma1.it luca.telesca@gmail.com Abstract We present DEFIE, an approach to large- scale Information Extraction (IE) based on a syntactic-semantic analysis of textual defini- tions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and fi- nally exploit the resulting information to orga- nize the acquired relations hierarchically. The output of DEFIE is a high-quality knowledge base consisting of several million automati- cally acquired semantic relations.1 1 Introduction The problem of knowledge acquisition lies at the core of Natural Language Processing. Recent years have witnessed the massive exploitation of collabo- rative, semi-structured information as the ideal mid- dle ground between high-quality, fully-structured resources and the larger amount of cheaper (but noisy) unstructured text (Hovy et al., 2013). Col- laborative projects, like Freebase (Bollacker et al., 2008) and Wikidata (Vrandeˇci´c, 2012), have been being developed for many years and are continu- ously being improved. A great deal of research also focuses on enriching available semi-structured re- sources, most notably Wikipedia, thereby creating taxonomies (Ponzetto and Strube, 2011; Flati et al., 2014), ontologies (Mahdisoltani et al., 2015) and se- mantic networks (Navigli and Ponzetto, 2012; Nas- tase and Strube, 2013). These solutions, however, 1http://lcl.uniroma1.it/defie are inherently constrained to small and often pre- specified sets of relations. A more radical approach is adopted in systems like TEXTRUNNER (Etzioni et al., 2008) and REVERB (Fader et al., 2011), which developed from the Open Information Extraction (OIE) paradigm (Etzioni et al., 2008) and focused on the unconstrained extraction of a large number of relations from massive unstructured corpora. Ul- timately, all these endeavors were geared towards addressing the knowledge acquisition problem and tackling long-standing challenges in the field, such as Machine Reading (Mitchell, 2005). While earlier OIE approaches relied mostly on dependencies at the level of surface text (Etzioni et al., 2008; Fader et al., 2011), more recent work has focused on deeper language understanding at the level of both syntax and semantics (Nakashole et al., 2012; Moro and Navigli, 2013) and tackled chal- lenging linguistic phenomena like synonymy and polysemy. However, these issues have not yet been addressed in their entirety. Relation strings are still bound to surface text, lacking actual semantic con- tent. Furthermore, most OIE systems do not have a clear and unified ontological structure and re- quire additional processing steps, such as statisti- cal inference mappings (Dutta et al., 2014), graph- based alignments of relational phrases (Grycner and Weikum, 2014), or knowledge base unification pro- cedures (Delli Bovi et al., 2015), in order for their potential to be exploitable in real applications. In DEFIE the key idea is to leverage the linguistic analysis of recent semantically-enhanced OIE tech- niques while moving from open text to smaller cor- pora of dense prescriptive knowledge. The aim is 529 Transactions of the …

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