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 […]
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 […]
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 […]
You cannot go straight from raw text to fitting a machine learning or deep learning model. You must clean your text first, which means splitting it into words and handling punctuation and case. In fact, there is a whole suite of text preparation methods that you may need to use, and the choice of methods […]
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 […]
You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard datasets that are well understood and widely used so […]
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 […]
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 […]
Deep learning is having a large impact on the field of natural language processing. But, as a beginner, where do you start? Both deep learning and natural language processing are huge fields. What are the salient aspects of each field to focus on and which areas of NLP is deep learning having the most impact? […]
Gentle introduction to the Encoder-Decoder LSTMs for sequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. Sequence-to-sequence prediction problems are challenging because the number of items in the input and output sequences can vary. For example, text translation and learning to execute […]
