TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling

Proceedings of SemEval-2016, pages 1320–1327, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling 1TU Darmstadt, LT Group, Germany Alexander Panchenko1, Stefano Faralli2, Eugen Ruppert1, Steffen Remus1, Hubert Naets3, C´edrick Fairon3, Simone Paolo Ponzetto2 and Chris Biemann1 2Mannheim University, Web and Data Science, Germany panchenko@lt.informatik.tu-darmstadt.de 3UCLouvain, CENTAL, Belgium Abstract We present a system for taxonomy construc- tion that reached the first place in all sub- tasks of the SemEval 2016 challenge on Tax- onomy Extraction Evaluation. Our simple yet effective approach harvests hypernyms with substring inclusion and Hearst-style lexico- syntactic patterns from domain-specific texts obtained via language model based focused crawling. Extracted taxonomies are evaluated on English, Dutch, French and Italian for three domains each (Food, Environment and Sci- ence). Evaluations against a gold standard and by human judgment show that our method out- performs more complex and knowledge-rich approaches on most domains and languages. Furthermore, to adapt the method to a new domain or language, only a small amount of manual labour is needed. 1 Introduction In this paper, we describe TAXI – a taxonomy in- duction method first presented at the SemEval 2016 challenge on Taxonomy Extraction Evaluation (Bor- dea et al., 2016). We consider taxonomy induction as a process that should – as much as possible – be driven solely on the basis of raw text process- ing. While some labeled examples might be uti- lized to tune the extraction and induction process, we avoid relying on structured lexical resources such as WordNet (Miller, 1995) or BabelNet (Navigli and Ponzetto, 2010). We rather envision a situation where a taxonomy shall be induced in a new domain or a new language for which such resources do not exist. In this paper, we demonstrate our methodol- ogy based on hyponym extraction from substrings and general-domain and domain-specific corpora for four languages and three domains. 2 Related Work The extraction of taxonomic relationships from text is a long-standing challenge in ontology learning, see e.g. Biemann (2005) for a survey. The literature on hypernym extraction offers a high variability of methods, from simple lexical patterns (Hearst, 1992; Oakes, 2005), similar to those used in our method, to complex statistical techniques (Agirre et al., 2000; Ritter et al., 2009). Snow et al. (2004) use sentences that contain two terms which are known to be hypernyms. They parse sentences and extract patterns from the parse trees. Finally, they train a hypernym classifier based on these features and applied to text corpora. Yang and Callan (2009) presented a semi- supervised taxonomy induction framework that integrates co-occurrence, syntactic dependencies, lexical-syntactic patterns and other features to learn an ontology metric, calculated in terms of the se- mantic distance for each pair of terms in a taxon- omy. Terms are incrementally clustered on the basis of their ontology metric scores. Snow et al. (2006) perform incremental construc- tion of taxonomies using a probabilistic model. They combine evidence from …

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