Machine Learning & Deep Linguistic Analysis in Text Analytics
Text analysis is becoming a pervasive task in many business areas. Machine Learning is the most common approach used in text analysis and is based...
What is the difference between stemming and lemmatization?
Stemming and lemmatization are methods used by search engines and chatbots to analyze the meaning behind a word. Stemming uses the stem of the word,...
Creating “Her”: Artificial Intelligence is here!
Have you seen the movie “Her”? If you are not sure we will summarize it in one sentence: a human falling in romantic love with a machine! The film...
Is it possible to speed up the training process in Deep Learning?
“Artificial Intelligence has arrived to stay!” You may have heard this over the past years several times, and it’s right. However, we are not...
Differences between Polarity and Topic-Based Sentiment Analysis
From a business perspective, there is a huge difference between plain polarity and topic-based sentiment analysis (also known as aspect-based...
Automatic IAB tagging enables now semantic ad targeting
Our Text Classification API supports IAB’s standard contextual taxonomy, enabling content tagging in compliance with this model in large volumes and...
On the Stanford parser (and Bitext parser)
In some of our recent talks, colleagues have asked us about the Stanford parser and how it compared to Bitext technology (namely at our last...