Word embeddings are representations of words in a vector space that encode the meaning of words such that similar words are close together. They can be generated using various techniques such as neural networks, dimensionality reduction and probabilistic models. Word embeddings have been shown to improve performance in NLP tasks such as syntactic parsing and sentiment analysis.
University of Washington
Winter 2022
This course provides a comprehensive overview of Natural Language Processing (NLP), including core components like text classification, machine translation, and syntax analysis. It offers two project types: implementation problem-solving for CSE 447, and reproducing experiments from recent NLP papers for CSE 517.
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