Displaying 1 to 8 from 8 results

gensim - Topic Modelling for Humans

  •    Python

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.

text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

awesome-text-summarization - The guide to tackle with the Text Summarization

  •    

The guide to tackle with the Text Summarization. To take the appropriate action, we need latest information. But on the contrary, the amount of the information is more and more growing. There are many categories of information (economy, sports, health, technology...) and also there are many sources (news site, blog, SNS...).

text-summarization-experiment - Experiment on text summarization techniques and exploring Tensorflow

  •    Jupyter

This project is our first attempt to make use of Tensorflow and specifically the textsum model. This model enables us to create article summaries in an automated way, which is one of the areas that we are currently researching. In the field of automated text summarization, Deep learning is currently the most promising approach. The whole project has been developed on Tensorflow 1.0.1. In the following sections you will find further details and instructions on how to complete the steps that were outlined above.




pythonrouge - Python wrapper for evaluating summarization quality by ROUGE package

  •    Perl

This is the python wrapper to use ROUGE, summarization evaluation toolkit. In this implementation, you can evaluate various types of ROUGE metrics. You can evaluate your system summaries with reference summaries right now. It's not necessary to make an xml file as in the general ROUGE package. However, you can evaluate ROUGE scores in a standard way if you saved system summaries and reference summaries in specific directories. In the document summarization research, recall or F-measure of ROUGE metrics is used in most cases. So you can choose either recall or F-measure or both of these of ROUGE evaluation result for convenience.

Text_Summarization_with_Tensorflow - Implementation of a seq2seq model for summarization of textual data

  •    Jupyter

Implementation of a seq2seq model for summarization of textual data using the latest version of tensorflow. Demonstrated on amazon reviews, github issues and news articles. I tried the network on three different datasets.

text-summarization-tensorflow - Tensorflow seq2seq Implementation of Text Summarization.

  •    Python

Simple Tensorflow implementation of text summarization using seq2seq library. Encoder-Decoder model with attention mechanism.