Bitext presents... its new API platform video!

[fa icon="calendar'] Jun 19, 2019 5:00:00 PM / by Bitext posted in API, Machine Learning, NLP, Semantic Analysis, Sentiment Analysis, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Text Categorization, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLG, Stemming, NLU, Query Rewriting, POS tagging, RASA, Segmentation, AI, Multilanguage, Language Identification, Entity extraction, Anonymization, Decompounding, Lemmatization, NLP for Core, NLP for Chatbots, NLP for CX

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Our NLP API platform is the most comprehensive and accurate (more than 90% accuracy) in the text analysis market. You can find a wide variety of multilingual NLP tools and solutions that will help you create the best customer experience for your business. Watch our new video now and sign up!

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Bitext: The Future of NLP according to Gartner

[fa icon="calendar'] Aug 23, 2018 5:31:18 PM / by Bitext posted in NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Chatbots, Stemming, POS tagging

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Powered by a linguistic approach, the future of natural language processing will enable human-like understanding through a wide range of applications. Thanks to Bitext NLP technologies, that far future is closer than expected. Bitext solutions are fully oriented to the current needs of many forward-looking companies relying on cutting-edge techniques ranging from sentiment analysis tools to a generation of artificial training data. After years of hard work in the field of the automation of customer support, Bitext developed the most advanced API ever seen and several conversational agents for innovative enterprises as, for instance, TechCrunch.

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What is the difference between stemming and lemmatization?

[fa icon="calendar'] Feb 28, 2018 4:16:00 PM / by Tita Risueño posted in Machine Learning, NLP, Bitext, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Stemming, AI, Multilanguage, Lemmatization, NLP for Core

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When we are running a search, we want to find relevant results not only for the exact expression we typed on the search bar, but also for the other possible forms of the words we used. For example, it’s very likely we will want to see results containing the form “skirt” if we have typed “skirts” in the search bar.

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