A Unified Taxonomy of Deep Syntactic Relations

This paper analyzes multiple deep-syntactic frameworks with the goal of creating a proposal for a set of universal semantic role labels. The proposal examines various theoretic linguistic perspectives and focuses on Meaning-Text Theory and Functional Generative Description frameworks. For the purpose of this research, data from four languages is used -- Spanish and Catalan (Taule et al., 2011), Czech (Hajic et al., 2017), and English (Hajic et al., 2012). This proposal is oriented towards Universal Dependencies (de Marneffe et al., 2021) with a further intention of applying the universal semantic role labels to the UD data.

AGRR-2019: A Corpus for Gapping Resolution in Russian

This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts. The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis. In this paper, we pay special attention to the gapping resolution methods that were introduced within the shared task as well as an alternative test set that illustrates that our corpus is a diverse and representative subset of Russian language gapping sufficient for effective utilization of machine learning techniques.