Why opting for an NLP Middleware is important for your business

[fa icon="calendar'] Aug 27, 2019 4:52:00 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, NLU, POS tagging, AI, Entity extraction, NLP for Core

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According to Gartner, companies working with AI should stop combining training and learning activities, one of the reasons being the slowing down of conversational agents’ learning processes. The most recommended course of action for data managers is exploring emerging middleware tools that allow them to use the same training data set for multiple AI service providers.

 

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Bitext mentioned in 4 Gartner Hype Cycle Reports in 2019

[fa icon="calendar'] Aug 20, 2019 4:36:00 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, NLU, POS tagging, AI, Entity extraction, NLP for Core

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When searching for innovative solutions, it is crucial for leaders and decision makers to have the information that allows them to make informed decisions. Bitext is currently at the forefront of technology since it has been mentioned lately in no less than 20 Gartner reports and was selected as Cool Vendor in AI Core Technologies in 2018. But we keep working hard and Gartner, once again, mentioned Bitext in 4 new Hype Cycle reports. 

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AI for Fraud detection in Trading: A Case Study

[fa icon="calendar'] Aug 7, 2019 4:20:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots, Finance, Banking

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Those familiar with stock markets surely know how traders communicate with each other via chatrooms in order to keep up with the trends and get actionable insights from peers. Most of these platforms, especially those offering free access, are a cluster of activity that needs to be moderated to ensure the interactions are civil and legal.  Why do it manually through  professional moderators when you can use an automated AI system?

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Knowledge Graph Generation for Financial Databases

[fa icon="calendar'] Jul 29, 2019 5:17:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots, Finance, Banking

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People who use financial databases are aware of the hardships of ensuring information is structured and legible. Don’t worry! Knowledge graphs are here to help.

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Chat-Based Search for Financial News in Natural Language

[fa icon="calendar'] Jul 3, 2019 5:12:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots, Finance, Banking

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Thousands of financial news headlines appear every day on your phone screen. Do you have time to read them all? We dare to say you probably don´t. What if you could ask your news provider to inform you on your interests? Now that’s possible thanks to our AI technology for chat-based financial news based on NLP.

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Artificial Training  Data for Chatbots

[fa icon="calendar'] Jun 25, 2019 5:47:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots

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When working on AI projects, owning data to nurture your solution is key for good performance. Gathering e-mails and conversation logs to train your bot may be as good as a makeshift solution, but this lack of data can now be cut off at the root. Why not start farming your own data instead of harvesting it?

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