LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures
This work addresses lexical complexity prediction for natural language processing applications, but it is incremental as it builds on existing methods with limited novelty.
The paper tackled the multi-word lexical complexity prediction task at SemEval-2021 by using a LightGBM model with features including bigram association measures, achieving honorable performance in the multi-word task but poorer results in the single-word task.
This paper describes the system developed by the Laboratoire d'analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent.