One of the most important and most-used functions in text analytics and NLP is sentiment analysis — the process of determining whether a word, phrase, or document is positive, negative, or neutral. The first step […]
One of the most important and most-used functions in text analytics and NLP is sentiment analysis — the process of determining whether a word, phrase, or document is positive, negative, or neutral. The first step […]
It pretty much started here: McCulloch and Pitts wrote a paper [1] describing an idealized neuron as a threshold logic device and showed that an arrangement of such devices could express any propositional logic formula. […]
From Tokenization to PoS Tagging and beyond, this high-level overview explains the basic functions of text analytics and natural language processing.
Discover how the top 6 text analytics vendors differ from one another and learn about which platform suits which use case. This comprehensive text analytics guide serves as the who’s who and the what’s what in natural language processing.
Machine learning micromodels reduce the challenges of sourcing and annotating data while delivering better precision and accuracy than macromodels.
Lexalytics Support Engineer Sarah Williams answers our users’ most frequently-asked questions about how to tune Lexalytics’ natural language processing.
What is natural language processing? And what does it mean for you, me and your drunk friend? Seth Redmore explains the fundamental concepts of NLP in 5 minutes or fewer.
We took our NLP engine and focused it on Reddit’s five year goals. The insights are fascinating.
What is a matrix, and what is it used for? This short article will attempt to de-mystify this complex mathematical concept for natural language processing.
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