Need for Word Embedding?
- To reduce dimensionality
- To use a word to predict the words around it.
- Inter-word semantics must be captured.
Word Embeddings in NLP
Word Embeddings are numeric representations of words in a lower-dimensional space, capturing semantic and syntactic information. They play a vital role in Natural Language Processing (NLP) tasks. This article explores traditional and neural approaches, such as TF-IDF, Word2Vec, and GloVe, offering insights into their advantages and disadvantages. Understanding the importance of pre-trained word embeddings, providing a comprehensive understanding of their applications in various NLP scenarios.