The Effectiveness of Simple Hybrid Systems for Hypernym Discovery

Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods. However, recent works utilizing a mix of these strategies have yielded state-of-the-art results. This paper evaluates the contribution of both paradigms to hybrid success by evaluating the benefits of hybrid treatment of baseline models from each paradigm. Even with a simple methodology for each individual system, utilizing a hybrid approach establishes new state-of-the-art results on two domain-specific English hypernym discovery tasks and outperforms all non-hybrid approaches in a general English hypernym discovery task.

[1]  Dominik Schlechtweg,et al.  Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection , 2016, EACL.

[2]  Marti A. Hearst Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[3]  Sharon A. Caraballo Automatic construction of a hypernym-labeled noun hierarchy from text , 1999, ACL.

[4]  Ido Dagan,et al.  Directional distributional similarity for lexical inference , 2010, Natural Language Engineering.

[5]  José Camacho-Collados Why we have switched from building full-fledged taxonomies to simply detecting hypernymy relations , 2017, ArXiv.

[6]  Ido Dagan,et al.  Improving Hypernymy Detection with an Integrated Path-based and Distributional Method , 2016, ACL.

[7]  Stephen Roller,et al.  Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora , 2018, ACL.

[8]  Tomoya Takatani,et al.  Distributional Hypernym Generation by Jointly Learning Clusters and Projections , 2016, COLING.

[9]  Tomas Mikolov,et al.  Enriching Word Vectors with Subword Information , 2016, TACL.

[10]  Gideon S. Mann Fine-Grained Proper Noun Ontologies for Question Answering , 2002, COLING 2002.

[11]  Gábor Berend,et al.  300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes , 2018, *SEMEVAL.

[12]  Daniel Jurafsky,et al.  Semantic Taxonomy Induction from Heterogenous Evidence , 2006, ACL.

[13]  Horacio Saggion,et al.  SemEval-2018 Task 9: Hypernym Discovery , 2018, *SEMEVAL.

[14]  Gabriel Bernier-Colborne,et al.  CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery , 2018, *SEMEVAL.

[15]  Daniel Jurafsky,et al.  Learning Syntactic Patterns for Automatic Hypernym Discovery , 2004, NIPS.

[16]  Douwe Kiela,et al.  Poincaré Embeddings for Learning Hierarchical Representations , 2017, NIPS.

[17]  Horacio Saggion,et al.  Supervised Distributional Hypernym Discovery via Domain Adaptation , 2016, EMNLP.

[18]  Ivan Vulić,et al.  Specialising Word Vectors for Lexical Entailment , 2017, NAACL.

[19]  Stefano Faralli,et al.  A Large DataBase of Hypernymy Relations Extracted from the Web , 2016, LREC.