LIdioms: A Multilingual Linked Idioms Data Set

LIDIOMS: A Multilingual Linked Idioms Data Set Diego Moussallem1,2, Mohamed Ahmed Sherif2,Diego Esteves1 Marcos Zampieri3, Axel-Cyrille Ngonga Ngomo2 1Faculty of Mathematics and Computer Science - University of Leipzig, Germany 2Data Science Group - University of Paderborn, Germany 3Research Group in Computational Linguistics - University of Wolverhampton, United Kingdom lastname@informatik.uni-leipzig.de Abstract In this paper, we describe the LIDIOMS data set, a multilingual RDF representation of idioms currently containing five languages: English, German, Italian, Portuguese, and Russian. The data set is intended to support natural language processing applications by providing links between idioms across languages. The underlying data was crawled and integrated from various sources. To ensure the quality of the crawled data, all idioms were evaluated by at least two native speakers. Herein, we present the model devised for structuring the data. We also provide the details of linking LIDIOMS to well-known multilingual data sets such as BabelNet. The resulting data set complies with best practices according to Linguistic Linked Open Data Community. Keywords: multilingual, idioms, translation 1. Introduction Recently, the Linguistic Linked Open Data (LLOD)1 move- ment has gained significant momentum. According to Mc- Crae et al. (2016), a large number of linguistic data sets have been extracted from various sources and been rep- resented as Linked Data (LD). This new movement was motivated by the novel capabilities of the LD paradigm pertaining to transforming, sharing, and linking linguistic data on the Web (Chiarcos et al., 2012). Resources such as dictionaries and knowledge bases are essential in the de- velopment of Natural Language Processing (NLP) systems. However, most of these resources are still bilingual on the LLOD. Thus, becoming worthwhile to develop multilin- gual knowledge bases by reusing these bilingual contents. Multilingualism is important not only for sharing informa- tion across Web but also for learning new concepts from other cultures. There are many data sets and linguistic resources available at LLOD, however, most of them do not contain much in- formation about Multiword Expressions (MWE). MWE are known to constitute a difficult problem on a number of NLP tasks such as machine translation, language genera- tion, and sentiment analysis/opinion mining. There are dif- ferent types of MWE, according to Nunberg et al. (1994), MWE are categorized as phrase verbs, compounds, fixed expression, semi-fixed expressions, idioms, slang, and oth- ers. MWE.This work focuses on idioms, a particular type of Most idioms are culture-bound and their senses come from particular concepts of everyday life to a given culture. By definition, idioms are a sequence of words whose meaning cannot be derived from the meaning of words that constitute them (Nunberg et al., 1994). Idioms are generally classified as non-compositional. One of the direct consequences of non-compositionality is the impossibility of translating this kind of word group literally (Nunberg et al., 1994) posing 1http://linguistics.okfn.org/ challenges to human translators and to machine translation systems. In this paper, we propose LIDIOMS, a multilingual linked data set of idioms in five languages. In LIDIOMS, we do not distinguish between idioms sub-categories and thus work on …

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