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 on statistical and mathematical models. Linguistic approaches, which are based on knowledge of language and its...
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, while lemmatization uses the context in which the word is being used. We’ll later go into more detailed...
“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 talking about a Hollywood science-fiction movie. We are referring to Machine and Deep Learning. The difference between...
Our Text Classification API supports IAB’s standard contextual taxonomy, enabling content tagging in compliance with this model in large volumes and with great speed, and easing the participation in the new online advertising ecosystem. The result is the impression of...
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 workshop on Semantic Analysis of Big Data in San Francisco, and in our presentation in the Semantic Garage also in San...
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