Synonymy in Bilingual Context: The CzEngClass Lexicon

Proceedings of the 27th International Conference on Computational Linguistics, pages 2456–2469 Santa Fe, New Mexico, USA, August 20-26, 2018. 2456 Synonymy in Bilingual Context: The CzEngClass Lexicon Zdeˇnka Urešová Eva Fuˇcíková Eva Hajiˇcová Jan Hajiˇc Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranské nám. 25, CZ-11800 Prague, Czech Republic {uresova,fucikova,hajicova,hajic}@ufal.mff.cuni.cz Abstract This paper describes CzEngClass, a bilingual lexical resource being built to investigate verbal synonymy in bilingual context and to relate semantic roles common to one synonym class to verb arguments (verb valency). In addition, the resource is linked to existing resources with the same or a similar aim: English and Czech WordNet, FrameNet, PropBank, VerbNet (SemLink), and valency lexicons for Czech and English (PDT-Vallex, Vallex and EngVallex). There are several goals of this work and resource: (a) to provide gold standard data for automatic experiments in the future (such as automatic discovery of synonym classes, word sense disambiguation, assignment of classes to occurrences of verbs in text, coreferential linking of verb and event arguments in text, etc.), (b) to build a core (bilingual) lexicon linked to existing resources, serving for comparative studies and possibly for training automatic tools, and (c) to enrich the annotation of a parallel treebank, the Prague Czech English Dependency Treebank, which so far contained valency annotation but has not linked synonymous senses of verbs together. The method used for extracting the synonym classes is a semi-automatic process with a substantial amount of manual work during filtering, role assignment to classes and individual class members’ arguments, and linking to the external lexical resources. We present the first version with 200 classes (about 1800 verbs) and evaluate interannotator agreement using several metrics. 1 Introduction Lexical resources, despite the fast progress in building end-to-end systems based on deep learning and artificial neural networks, are an important piece of the puzzle in Computational Linguistics and Nat- ural Language Processing (NLP). They provide information that humans need to understand relations between words as well as the usage of these words in text. In addition, they can help various NLP tasks. This is why lexicons like WordNet (Miller, 1995; Fellbaum, 1998; Pala et al., 2011), FrameNet (Baker et al., 1998; Fillmore et al., 2003), VerbNet (Schuler, 2006), PropBank (Palmer et al., 2005) or EngVallex (Cinková, 2006; Cinková et al., 2014) have been created. They are for English, but there are also similar resources for other languages, often in a multilingual setting: WordNet is available in many languages (Vossen, 2004; Fellbaum and Vossen, 2012), Predicate Matrix (Lopez de Lacalle et al., 2016) extend coverage of several verbal resources and adds more romance languages, FrameNet has been extended to multiple languages (Boas, 2009), or there is the bilingual valency lexicon CzEngVallex for the case of Czech and English (Urešová et al., 2016). One might thus question why it is necessary to develop another resource, and not just to connect some additional information to one or more of existing resources. After a thorough review (cf. also Sect. 2), …

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