Archive | Deep Learning for Natural Language Processing

What Are Word Embeddings for Text?

What Are Word Embeddings for Text?

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 […]

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A Gentle Introduction to the Bag-of-Words Model

A Gentle Introduction to the Bag-of-Words Model

The bag-of-words model is a way of representing text data when modeling text with machine learning algorithms. The bag-of-words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification. In this tutorial, you will discover the bag-of-words model for feature extraction in natural language […]

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Scatter Plot of PCA Projection of Word2Vec Model

How to Develop Word Embeddings in Python with Gensim

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 […]

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How to Use Word Embedding Layers for Deep Learning with Keras

How to Use Word Embedding Layers for Deep Learning with Keras

Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this […]

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How to Prepare Text Data for Machine Learning with scikit-learn

How to Encode Text Data for Machine Learning with scikit-learn

Text data requires special preparation before you can start using it for predictive modeling. The text must be parsed to remove words, called tokenization. Then the words need to be encoded as integers or floating point values for use as input to a machine learning algorithm, called feature extraction (or vectorization). The scikit-learn library offers […]

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Promise of Deep Learning for Natural Language Processing

Promise of Deep Learning for Natural Language Processing

The promise of deep learning in the field of natural language processing is the better performance by models that may require more data but less linguistic expertise to train and operate. There is a lot of hype and large claims around deep learning methods, but beyond the hype, deep learning methods are achieving state-of-the-art results on […]

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7 Applications of Deep Learning for Natural Language Processing

7 Applications of Deep Learning for Natural Language Processing

The field of natural language processing is shifting from statistical methods to neural network methods. There are still many challenging problems to solve in natural language. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific language problems. It is not just the performance of deep learning models on benchmark problems that is most […]

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