LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction

• URL: http://arxiv.org/abs/2105.08780v1
• Date: Tue, 18 May 2021 18:55:04 GMT
• Title: LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction
• Authors: Abhinandan Desai and Kai North and Marcos Zampieri and Christopher M. Homan
• Abstract summary: This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP) Our system uses logistic regression and a wide range of linguistic features to predict the complexity of single words in this dataset. We evaluate the results in terms of mean absolute error, mean squared error, Pearson correlation, and Spearman correlation.
• Score: 4.86331990243181
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP). The task organizers provided participants with an augmented version of CompLex (Shardlow et al., 2020), an English multi-domain dataset in which words in context were annotated with respect to their complexity using a five point Likert scale. Our system uses logistic regression and a wide range of linguistic features (e.g. psycholinguistic features, n-grams, word frequency, POS tags) to predict the complexity of single words in this dataset. We analyze the impact of different linguistic features in the classification performance and we evaluate the results in terms of mean absolute error, mean squared error, Pearson correlation, and Spearman correlation.

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