Abstract

When dealing with languages and words, we might end up classifying texts across thousands of classes, for use in multiple natural language processing (NLP) tasks. Much research has been undertaken in this field in recent years, and this has resulted in the transformation of words in languages to the format of vectors that can be used in multiple sets of algorithms and processes. This chapter offers an in-depth explanation of word embeddings and their effectiveness. We introduce their origin and compare the different models used to accomplish various NLP tasks.

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