NLP/Text Analytics Archives

NLP/Text Analytics

Linked Data and Data Science

Modeling NLP/Text Analytics Statistics Tools Tools & Languagesposted by Diego Arenas, ODSC November 25, 2017

Understanding Gender Roles in Movies with Text Mining

Modeling NLP/Text Analytics Data Mining|Life|text analyticsposted by Julia Silge November 24, 2017

I have a new visual essay up at The Pudding, using text mining to explore how women are portrayed in film. In April 2016, we broke down film dialogue by gender. The essay presented an imbalance in which men delivered more lines than women across 2,000 screenplays. But... Read more

Natural Language Processing in a Kaggle Competition for Movie Reviews

Modeling NLP/Text Analyticsposted by Jesse Steinweg-Woods November 22, 2017

I decided to try playing around with a Kaggle competition. In this case, I entered the “When bag of words meets bags of popcorn” contest. This contest isn’t for money; it is just a way to learn about various machine learning approaches. The competition was trying to showcase... Read more

Word2Vec – the world of word vectors

Modeling NLP/Text Analytics R Tools & Languages Data Visualization|NLPposted by Stefan Nikolić November 19, 2017

Have you ever wondered how a chatbot can learn about the meaning of words in a text? Does this sound interesting? Well, in this blog we will describe a very powerful method, Word2Vec, that maps words to numbers (vectors) in order to easily capture and distinguish their meaning. We will briefly describe how Word2Vec... Read more

Word embeddings in 2017: Trends and future directions

Modeling NLP/Text Analytics Natural Language Processing|text analyticsposted by Sebastian Ruder November 10, 2017

Table of contents: Subword-level embeddings OOV handling Evaluation Multi-sense embeddings Beyond words as points Phrases and multi-word expressions Bias Temporal dimension Lack of theoretical understanding Task and domain-specific embeddings Embeddings for multiple languages Embeddings based on other contexts The word2vec method based on skip-gram with negative... Read more

Enhancing Customer Experience with NLP — Part 2

Modeling NLP/Text Analyticsposted by Andrew Prokop November 8, 2017

See first-hand how you can bring an NLP application to life. Last week I introduced No Jitter readers to Natural Language Processing (NLP) and Facebook’s free NLP service, wit.ai. I wrote about intents and entities, and how they work together to convert human language into actionable... Read more

Using The Newspaper Library to Scrape News Articles

Data Wrangling Modeling NLP/Text Analytics Tools Tools & Languages Natural Language Processingposted by George McIntire, ODSC October 12, 2017

This post is the first of a two-part series in which we apply NLP techniques to analyze articles about big data, data science, and AI. If you are tired of the hassles of web scraping, then this post might be just for you. I occasionally web scrape news... Read more

Multi-Task Learning Objectives for Natural Language Processing

Modeling NLP/Text Analytics Natural Language Processingposted by Sebastian Ruder October 3, 2017

In a previous blog post, I discussed how multi-task learning (MTL) can be used to improve the performance of a model by leveraging a related task. Multi-task learning consists of two main components: a) The architecture used for learning and b) the auxiliary task(s) that are trained... Read more

Enhancing Customer Experience with Natural Language Processing

Modeling NLP/Text Analytics Natural Language Processingposted by Andrew Prokop September 18, 2017

Processing language into actionable components is the future of communication. If you talk to a man in a language he understands, that goes to his head. If you talk to him in his language, that goes to his heart. — Nelson Mandela I would venture to... Read more

Word Vectors and SAT Analogies

Blog NLP/Text Analyticsposted by Aaron Schumacher July 23, 2017

This reminded me of SAT analogy questions, which disappeared from the SAT in 2005, but looked like this: PALTRY : SIGNIFICANCE :: A. redundant : discussion B. austere : landscape C. opulent : wealth D. oblique : familiarity E. banal : originality The king/queen example is not difficult, and... Read more