Designing the ELEXIS Parallel Sense-Annotated Dataset in 10 European Languages
Federico Martelli, Roberto Navigli, Simon Krek, Jelena Kallas, Polona Gantar, Svetla Koeva, Sanni Nimb, Bolette Sandford Pedersen, Sussi Olsen, Margit Langemets, Kristina Koppel, Tiiu Üksik, Kaja Dobrovoljc, Rafael Ureña-Ruiz, José-Luis Sancho-Sánchez, Veronika Lipp, Tamás Váradi, Győrffy, András, László Simon, Valeria Quochi · Research at the University of Copenhagen (University of Copenhagen) · 2021
Over the course of the last few years, lexicography has witnessed the burgeoning of increasingly reliable automatic approaches supporting the creation of lexicographic resources such as dictionaries, lexical knowledge bases and annotated datasets. In fact, recent achievements in the field of Natural Language Processing and particularly in Word Sense Disambiguation have widely demonstrated their effectiveness not only for the creation of lexicographic resources, but also for enabling a deeper analysis of lexical-semantic data both within and across languages. Nevertheless, we argue that the potential derived from the connections between the two fields is far from exhausted. In this work, we address a serious limitation affecting both lexicography and Word Sense Disambiguation, i.e. the lack of high-quality sense-annotated data and describe our efforts aimed at constructing a novel entirely manually annotated parallel dataset in 10 European languages. For the purposes of the present paper, we concentrate on the annotation of morpho-syntactic features. Finally, unlike many of the currently available sense-annotated datasets, we will annotate semantically by using senses derived from high-quality lexicographic repositories.