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District Data Labs

Forward Propagation: Building a Skip-Gram Net From the Ground Up

Part 1: Skip-gram Feedforward

Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out the other posts in the series so far:

Let's continue our treatment of the . . .

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January 13, 2017

NLP Research Lab Part 2: Skip-Gram Architecture Overview

Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out NLP Research Lab Part 1: Distributed Representations.

Chances are, if you’ve been working in Natural Language Processing (NLP) or machine learning, you’ve heard of the class of approaches called . . .

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August 03, 2016

NLP Research Lab Part 1: Distributed Representations

How I Learned To Stop Worrying And Love Word Embeddings

Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab.

This post is about Distributed Representations, a concept that is foundational not only to the understanding of data processing in machine learning, but also to the understanding of information processing and storage . . .

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July 27, 2016

Beyond the Word Cloud

Visualizing Text with Python

In this article, we explore two extremely powerful ways to visualize text: word bubbles and word networks. These two visualizations are replacing word clouds as the defacto text visualization of choice because they are simple to create, understandable, and provide deep and valuable at-a-glance insights. In this post, we will examine how to . . .

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July 26, 2016

Preparing for NLP with NLTK and Gensim

PyCon 2016 Tutorial on Sunday May 29, 2016 at 9am

This post is designed to point you to the resources that you need in order to prepare for the NLP tutorial at PyCon this coming weekend! If you have any questions, please contact us according to the directions at the end of the post.

In this tutorial, we will explore the features of the NLTK library for text processing in order to build . . .

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Posted in: nlppython

May 25, 2016

Modern Methods for Sentiment Analysis

Sentiment analysis is a common application of Natural Language Processing (NLP) methodologies, particularly classification, whose goal is to extract the emotional content in text. In this way, sentiment analysis can be seen as a method to quantify qualitative data with some sentiment score. While sentiment is largely subjective, sentiment . . .

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Posted in: pythonnlp

March 31, 2015

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