Tapping BERT for Preposition Sense Disambiguation
This work addresses a key problem in natural language processing for tasks like semantic role labeling and question answering, but it is incremental as it applies existing BERT models to a specific disambiguation task.
The paper tackles preposition sense disambiguation by proposing a method using pre-trained BERT and its variants, achieving an accuracy of 86.85% on the SemEval-2007 dataset, which surpasses the state-of-the-art.
Prepositions are frequently occurring polysemous words. Disambiguation of prepositions is crucial in tasks like semantic role labelling, question answering, text entailment, and noun compound paraphrasing. In this paper, we propose a novel methodology for preposition sense disambiguation (PSD), which does not use any linguistic tools. In a supervised setting, the machine learning model is presented with sentences wherein prepositions have been annotated with senses. These senses are IDs in what is called The Preposition Project (TPP). We use the hidden layer representations from pre-trained BERT and BERT variants. The latent representations are then classified into the correct sense ID using a Multi Layer Perceptron. The dataset used for this task is from SemEval-2007 Task-6. Our methodology gives an accuracy of 86.85% which is better than the state-of-the-art.