Description

---

"ML for Multi-Word Expressions in Indian Languages" is a comprehensive and groundbreaking study that focuses on the application of Machine Learning (ML) techniques to address the challenges posed by Multi-Word Expressions (MWEs) in various Indian languages. MWEs, such as idioms, collocations, and phrasal verbs, are linguistic phenomena that consist of multiple words functioning as a single unit with a distinct meaning.This research project delves into the intricacies of MWEs within the context of Indian languages, which are renowned for their rich and diverse linguistic heritage. The aim is to develop robust ML algorithms and models that can accurately identify, extract, and interpret MWEs present in text corpora written in languages like Hindi, Tamil, Bengali, Telugu, and more.The study embraces a data-driven approach, utilizing large-scale annotated datasets and employing state-of-the-art ML techniques, including deep learning, natural language processing (NLP), and statistical modeling. By harnessing the power of ML, the research seeks to overcome the complexities and variations inherent in MWEs across different Indian languages, including variations in syntax, morphological structure, and semantic composition.Moreover, this research endeavor recognizes the importance of MWEs in various language-related applications, such as machine translation, sentiment analysis, information retrieval, and language generation. By effectively leveraging ML techniques, the project aims to enhance the performance and accuracy of these applications, ultimately facilitating better language understanding and communication in the Indian linguistic landscape.Overall, "ML for Multi-Word Expressions in Indian Languages" strives to make significant contributions to the field of computational linguistics, fostering advancements in ML-based approaches for handling MWEs and bridging the gap between Indian languages and cutting-edge language technologies.