RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 640–649 Bangkok, Thailand (online), August 5–6, 2021. ©2021 Association for Computational Linguistics 640 RS GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction Regina Stodden Dept. of Computational Linguistics Heinrich Heine University D¨usseldorf, Germany regina.stodden@hhu.de Gayatri Venugopal Symbiosis Institute of Computer Studies and Research Symbiosis International (Deemed University) Pune, Maharashtra, India gayatri.venugopal@sicsr.ac.in Abstract We present the technical report of the system called RS GV at SemEval-2021 Task 1 on com- plexity prediction of English words. RS GV is a neural network using hand-crafted linguis- tic features in combination with character and word embeddings to predict the target words’ complexity. For the generation of the hand- crafted features, we set the target words in relation to their senses. RS GV predicts the complexity well of biomedical terms but it has problems with the complexity prediction of very complex and very simple target words. 1 Introduction Text simplification is the process of modifying a text so that it becomes easy for the reader to un- derstand the meaning of the text without any loss of information. A main part of text simplification is lexical simplification. In lexical simplification, complex words are replaced with easier or more fre- quent synonyms. Following Shardlow (2014), the process of lexical simplification can be split as fol- lows: I.) identification of complex words in a given text, II.) substitution generation, III.) word sense disambiguation, IV.) synonym ranking, V.) substi- tution of complex word with the best synonym in correct morphological form. Following Shardlow (2014), the most common errors in lexical simplification are that the words are not identified as complex or that words are incor- rectly identified as complex. One reason might be the approach to predict complex words. So far, in the task called complex word identification (CWI), a word in a sentence was labeled as either complex or simple without any range in between. Shard- low et al. (2020) criticize this approach because there is no clear threshold when a word starts to be complex. Hence, they propose a new task called lexical complexity prediction (LCP). The aim of LCP is to predict the complexity of a single word or a multi-word expression on a scale of 0 to 1. This paper proposes RS GV , a model for LCP in the context of the SemEval-2021 task 1 (Shardlow et al., 2021a). RS GV uses hand-crafted features relative to their WordNet senses, Flair embeddings and a neural regressor in a cross-domain and within- domain setting. 2 Related Work Lexical complexity prediction is a new sub-task of lexical text simplification. The aim is to predict the complexity of a single word or a multiword expres- sion on a scale of 0 to 1. The most similar task is CWI. In contrast to LCP, CWI aims at binary classi- fication that determines whether a word is complex or not. As LCP has been mentioned for the first time in the context of this shared task …

Content truncated to 500 words