Search results for "embedding"

How to Develop a Word-Level Neural Language Model and Use it to Generate Text

How to Develop a Word-Level Neural Language Model and Use it to Generate Text

A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can […]

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How to Get Started with Deep Learning for Natural Language Processing

How to Get Started with Deep Learning for Natural Language Processing

Deep Learning for NLP Crash Course. Bring Deep Learning methods to Your Text Data project in 7 Days. We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances. Working with text is hard as it requires drawing upon knowledge from diverse domains such as linguistics, machine learning, statistical […]

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How to Develop Word-Based Neural Language Models in Python with Keras

How to Develop Word-Based Neural Language Models in Python with Keras

Language modeling involves predicting the next word in a sequence given the sequence of words already present. A language model is a key element in many natural language processing models such as machine translation and speech recognition. The choice of how the language model is framed must match how the language model is intended to […]

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Gentle Introduction to Statistical Language Modeling and Neural Language Models

Gentle Introduction to Statistical Language Modeling and Neural Language Models

Language modeling is central to many important natural language processing tasks. Recently, neural-network-based language models have demonstrated better performance than classical methods both standalone and as part of more challenging natural language processing tasks. In this post, you will discover language modeling for natural language processing. After reading this post, you will know: Why language […]

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How to Develop a Word Embedding Model for Predicting Movie Review Sentiment

Deep Convolutional Neural Network for Sentiment Analysis (Text Classification)

Develop a Deep Learning Model to Automatically Classify Movie Reviews as Positive or Negative in Python with Keras, Step-by-Step. Word embeddings are a technique for representing text where different words with similar meaning have a similar real-valued vector representation. They are a key breakthrough that has led to great performance of neural network models on […]

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Best Practices for Document Classification with Deep Learning

Best Practices for Text Classification with Deep Learning

Text classification describes a general class of problems such as predicting the sentiment of tweets and movie reviews, as well as classifying email as spam or not. Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some […]

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How to Develop a Deep Learning Bag-of-Words Model for Predicting Sentiment in Movie Reviews

How to Develop a Deep Learning Bag-of-Words Model for Sentiment Analysis (Text Classification)

Movie reviews can be classified as either favorable or not. The evaluation of movie review text is a classification problem often called sentiment analysis. A popular technique for developing sentiment analysis models is to use a bag-of-words model that transforms documents into vectors where each word in the document is assigned a score. In this […]

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How to Prepare Movie Review Data for Sentiment Analysis

How to Prepare Movie Review Data for Sentiment Analysis (Text Classification)

Text data preparation is different for each problem. Preparation starts with simple steps, like loading data, but quickly gets difficult with cleaning tasks that are very specific to the data you are working with. You need help as to where to begin and what order to work through the steps from raw data to data […]

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