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9 - Implementing Text Classification Using Word Embeddings

Published online by Cambridge University Press:  01 February 2024

Mihai Surdeanu
Affiliation:
University of Arizona
Marco Antonio Valenzuela-Escárcega
Affiliation:
University of Arizona
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Summary

In the previous chapter, we introduced word embeddings, which are real-valued vectors that encode semantic representation of words. We discussed how to learn them and how they capture semantic information that makes them useful for downstream tasks. In this chapter, we show how to use word embeddings that have been pretrained using a variant of the algorithm discussed in the previous chapter. We show how to load them, explore some of their characteristics, and show their application for a text classification task.

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Deep Learning for Natural Language Processing
A Gentle Introduction
, pp. 132 - 146
Publisher: Cambridge University Press
Print publication year: 2024

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