NLP research notes
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This tutorial contains an introduction to word embeddings. You will train your own word embeddings using a simple Keras model for a sentiment classification task, and then visualize them in the Embedding Projector
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https://www.tensorflow.org/text/guide/word_embeddings
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Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric.
This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model.
The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods.
In this tutorial, you will discover how to encode categorical data when developing neural network models in Keras.
After completing this tutorial, you will know:
(1) The challenge of working with categorical data when using machine learning and deep learning models.
(2) How to integer encode and one hot encode categorical variables for modeling.
(3) How to learn an embedding distributed representation as part of a neural network for categorical variables.
https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/
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Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation.
They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems.
In this post, you will discover the word embedding approach for representing text data.
After completing this post, you will know:
(1) What the word embedding approach for representing text is and how it differs from other feature extraction methods.
(2) That there are 3 main algorithms for learning a word embedding from text data.
(3) That you can either train a new embedding or use a pre-trained embedding on your natural language processing task.
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https://machinelearningmastery.com/what-are-word-embeddings/
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How to Use Word Embedding Layers for Deep Learning with Keras
https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/
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Word embeddings are a modern approach for representing text in natural language processing.
Word embedding algorithms like word2vec and GloVe are key to the state-of-the-art results achieved by neural network models on natural language processing problems like machine translation.
In this tutorial, you will discover how to train and load word embedding models for natural language processing applications in Python using Gensim.
After completing this tutorial, you will know:
(1) How to train your own word2vec word embedding model on text data.
(2) How to visualize a trained word embedding model using Principal Component Analysis.
(3) How to load pre-trained word2vec and GloVe word embedding models from Google and Stanford.
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https://machinelearningmastery.com/develop-word-embeddings-python-gensim/
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