Nested Noun Phrase Detection in English Text with BERT

Sammanfattning: In this project, we address the task of nested noun phrase identification in English sentences, where a phrase is defined as a group of words functioning as one unit in a sentence. Prior research has extensively explored the identification of various phrases for language understanding and text generation tasks. Our aim is to tackle the novel challenge of identifying nested noun phrases within sentences. To accomplish this, we first review existing work on related topics such as partial parsing and noun phrase identification. Subsequently, we propose a novel approach based on transformer models to recursively identify nested noun phrases in sentences. We fine-tune a pre-trained uncased BERT model to detect phrase structures in a sentence and determine whether they represent noun phrases. Our recursive approach involves merging relevant segments of a sentence and assigning labels to the noun phrases at each step, facilitating the identification of nested structures. The evaluation of our model demonstrates promising results, achieving a high accuracy of up to 93.6% when considering all noun phrases in isolation and 90.9% when accounting for the predicted phrase structure of the sentence. Additionally, our model exhibits a recall rate of 83.5% and 81.2% at both levels, respectively. Overall, our model proves to be effective in identifying nested noun phrases, showcasing the potential of transformer-based models in phrase structure identification. Future research should explore further applications and enhancements of such models in this domain.