Pay Attention when you Pay the Bills. A Multilingual Corpus with Dependency-based and Semantic Annotation of Collocations.

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4012–4019 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 4012 Pay attention when you pay the bills. A multilingual corpus with dependency-based and semantic annotation of collocations. Marcos Garcia Universidade da Coru˜na Grupo LyS, Dpto. de Letras Campus da Zapateira, Coru˜na Marcos Garc´ıa-Salido Universidade da Coru˜na Grupo LyS, Dpto. de Letras Campus da Zapateira, Coru˜na Susana Sotelo Universidade da Coru˜na Grupo LyS, Dpto. de Letras Campus da Zapateira, Coru˜na Estela Mosqueira Universidade da Coru˜na Grupo LyS, Dpto. de Letras Campus da Zapateira, Coru˜na {marcos.garcia.gonzalez,marcos.garcias susana.sotelo,estela.mosqueira,margarita.alonso}udc.gal Margarita Alonso-Ramos Universidade da Coru˜na Grupo LyS, Dpto. de Letras Campus da Zapateira, Coru˜na Abstract This paper presents a multilingual corpus with semantic annotation of collocations in En- glish, Portuguese, and Spanish. The whole resource contains 155k tokens and 1, 526 collocations labeled in context. The anno- tated examples belong to three syntactic struc- tures (adjective-noun, verb-object, and nomi- nal compounds), and represent 60 lexical func- tions in the Meaning-Text Theory (e.g., Oper, Magn, Bon, etc.). Each collocation was anno- tated by three linguists and the final resource was revised by a team of experts. The resulting corpora, which are freely available, can serve as a basis to evaluate different approaches for collocation identification and classification in three languages, which in turn can be useful for different NLP tasks such as natural lan- guage generation or understanding. 1 Introduction The automatic identification of collocations, as well as other multiword expressions (MWEs), is crucial for many natural language processing (NLP) tasks, since their linguistic behaviour dif- fers from other combinations of words (Mel’ˇcuk, 1995; Sag et al., 2002; Ramisch and Villavicen- cio, 2018). On the one hand, approaches to natural language generation may take advantage of collo- cational information to produce natural utterances with the desired meanings (Wanner et al., 2010; Lareau et al., 2011). In this regard, while different adjectives such as heavy and strong can convey ba- sically the same meaning (e.g., ‘intensification’ in heavy load and in strong fragrance), great has dif- ferent senses in great loss and in great time (with ‘intensification’ and ‘positive’ meanings, respec- tively). On the other hand, to interpret the meaning of a sentence, a system should take into account the properties of these expressions: for instance, the meaning of the verb [to] take in the colloca- tion take [a] cab is different from the same verb in a free combination such as take [a] pencil, so natural language understanding or abstract mean- ing representation systems could benefit from the correct identification of collocations (Bonial et al., 2014; O’Gorman et al., 2018). It is worth mention- ing that collocations are pervasive and frequent in all domains and text typologies, so their correct interpretation should be critical to progress in the automatic processing of natural languages. The concept of collocation was formalized in the Meaning-Text Theory as a combination of two lexical units (LUs) where one of them (the BASE, e.g., …

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