Automated Phrasal Verb and Key-Phrase Checking with LSTM-Based Attention Mechanism

Abstract

Text prediction and classification are crucial tasks in modern Natural Language Processing (NLP) techniques. Long short-term memory (LSTM), a type of Recurrent Neural Network (RNN), is well-known for its outstanding performance in text classification. Phrasal verbs, also known as Bagdhara in Bangla, play a vital role in making language more expressive and poetic in any language, including Bangla. These two or three-word phrases help us convey our emotions and thoughts more effectively. However, determining whether a phrase is a phrasal verb and appropriate for a given context can be challenging for writers, poets, and the general public. To address this issue, an automatic system capable of identifying and using phrasal verbs is necessary. In this study, we propose a system that can instantly and accurately predict phrasal verbs using the LSTM algorithm, a part of the RNN, and an attention mechanism. Our system achieved an overall phrasal verb prediction accuracy of 78.63%.