How chatbots enhance customer experience in contact centers

[fa icon="calendar'] Sep 9, 2019 5:03:00 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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Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.

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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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Evaluate the Quality of your Chatbots and Conversational Agents

[fa icon="calendar'] Jul 16, 2019 5:07:00 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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Chatbots require large amounts of training data to perform correctly. If you want your chatbot to recognize a specific intent, you need to provide a large number of sentences that express that intent, usually generated by hand. This manual generation is error-prone and can cause erroneous results. How can we solve it? With artificially-generated data.

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