Last Updated on December 19, 2019 BLEU, or the Bilingual Evaluation Understudy, is a score for comparing a candidate translation of text to one or more reference translations. Although developed for translation, it can be used to evaluate text generated for a suite of natural language processing tasks. In this tutorial, you will discover the BLEU score for evaluating and scoring candidate text using the NLTK library in Python. After completing this tutorial, you will know: A gentle introduction to […]

Last Updated on August 7, 2019 Caption generation is the challenging artificial intelligence problem of generating a human-readable textual description given a photograph. It requires both image understanding from the domain of computer vision and a language model from the field of natural language processing. It is important to consider and test multiple ways to frame a given predictive modeling problem and there are indeed many ways to frame the problem of generating captions for photographs. In this tutorial, you […]

Last Updated on September 3, 2020 Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right order. Recently, deep learning methods have achieved state of the art results on examples of this problem. It can […]

Last Updated on August 7, 2019 Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster. In this post, you will discover the problem of text summarization in natural language processing. After reading this post, you will know: Why text summarization is […]

Last Updated on August 7, 2019 Text summarization is the task of creating short, accurate, and fluent summaries from larger text documents. Recently deep learning methods have proven effective at the abstractive approach to text summarization. In this post, you will discover three different models that build on top of the effective Encoder-Decoder architecture developed for sequence-to-sequence prediction in machine translation. After reading this post, you will know: The Facebook AI Research model that uses the Encoder-Decoder model with a […]

Last Updated on August 7, 2019 Text summarization is the task of creating a short, accurate, and fluent summary of an article. A popular and free dataset for use in text summarization experiments with deep learning methods is the CNN News story dataset. In this tutorial, you will discover how to prepare the CNN News Dataset for text summarization. After completing this tutorial, you will know: About the CNN News dataset and how to download the story data to your […]

Last Updated on August 7, 2019 Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document. The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization. It can be difficult to apply this architecture in the Keras deep learning library, given some of the flexibility sacrificed to make the library clean, simple, and easy to use. In this […]