TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity
Sebastian Gombert, Sabine Bartsch · 2021
In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction (Shardlow et al., 2021a).The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a given sentence context, a continuous value indicating the difficulty in understanding a respective utterance.Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word-and character bigram frequencies and inclusion in word lists to create a model able to assign a word or multiword expression a context-dependent complexity score.We can show that especially contextualised string embeddings (Akbik et al., 2018) can help with predicting lexical complexity.