textminingNLP

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Every day, companies see thousands of emails, texts, tweets and other documents, but all the data in the world is useless if a company doesn't know what to do with it or if they can't quickly take action based on insights, opportunities and even potential disasters hidden among the data. Attensity reveals and analyzes these insights in real-time for businesses to make smarter, faster decisions across the enterprise. 

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Discover the Value of Text Analytics

Natural language processing (NLP) sounds cool in theory. We’re familiar with Siri and Echo of course, but where does it play a role in other companies? In today’s episode, we speak with Samuir Rahman from Mattermark, whose entire business model is predicated on organizing and making findable information about companies, and generating a platform to search by unique criterion. Doing so involves some conceptual work with NLP to make things findable. Samuir talks about what Mattermark is doing with this technology now and where he thinks the future may take the field, and interesting topic for investors and founders alike.

How Natural Language Processing Helps Mattermark Find Business Opps

This podcast features Doug Thompson, executive architect, Information Governance and master data management (MDM), at IBM. Thompson discusses the various factors that make data integration challenging and how one can overcome them. Thompson also shares his views on hybrid- and cloud-based data integration and the opportunity to become highly efficient.

Discover Data Integration solutions from IBM. http://www.ibm.com/analytics/us/en/technology/data-integration/

Download the Gartner report that positions IBM as a Leader in the 2016 Gartner Magic Quadrant for Data Quality Tools.
https://www-01.ibm.com/marketing/iwm/dre/signup?source=ibm-analytics&S_PKG=ov18607&S_TACT=C33603KW&dynform=4694&lang=en_US

Trust your data: The data integration challenge

It seems like every day there's more and more machine learning problems that involve learning on text data, but text itself makes for fairly lousy inputs to machine learning algorithms.  That's why there are text vectorization algorithms, which re-format text data so it's ready for using for machine learning.  In this episode, we'll go over some of the most common and useful ways to preprocess text data for machine learning.

Feature Processing for Text Analytics

Word2Vec is probably the go-to algorithm for vectorizing text data these days.  Which makes sense, because it is wicked cool.  Word2Vec has it all: neural networks, skip-grams and bag-of-words implementations, a multiclass classifier that gets swapped out for a binary classifier, made-up dummy words, and a model that isn't actually used to predict anything (usually).  And all that's before we get to the part about how Word2Vec allows you to do algebra with text.  Seriously, this stuff is cool.

Word2Vec

In this episode, we interviewed [Peter Murray Rust](https://twitter.com/thecontentmine?lang=en), chemist at Cambridge University. Peter is also known for his work and support related to open access and open data, among his projects is the [Content Mine](http://contentmine.org/) software chain about which we talked in this episode. The Content Mine group currently offer and maintain these open source software, but it also offers consulting services to assist individuals or groups interested in the suite of software.

Content Mine is a suite of open source software designed to mine and analyze the scientific literature. Three packages are currently offered by the [Content Mine group](https://github.com/ContentMine): getpapers, ami and norma. These 3 packages should allow us to download large sets of papers about a certain subject, normalize the obtained data to better explore it and then start analyzing using basic tools such as word counts and regular expressions. We explored and discussed these packages and how they could serve a researcher. You will also learn about the history of ContentMine, its team and the opinion of publishers, such as Elsevier, regarding such practices.

Blogpost: http://blog.colperscience.com/contentmine

Content Mine: scientific literature exploration through text mining

Episode 14: Content Mine: scientific literature exploration through text mining

Professor Oded Netzer discusses how the emerging science of text mining is helping marketers measure their impact — and the surprising insights the field may offer the healthcare industry.

textminingNLP

Ranga Eunny