An Investigation on Morphological Splitting and Word Embeddings in Natural Language Processing

This study delves into the topic of splitting, in natural language processing (NLP) using data examining its significance, difficulties and the impact of such data. The research examines instances of splitting in English well as other inflectional languages covering both derivations and inflections. It explores how incorporating word embeddings can enhance the analysis of word structure and semantic relationships. The results emphasize the advantages of integrating data and word embeddings into algorithms for splitting, which can improve tasks like determining word meanings and generating language. In conclusion this research contributes to our understanding of splitting, in NLP by highlighting its importance, challenges and potential enhancements through the use of data and word embeddings.