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

[fa icon="comment"] 0 Comments

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!

Read More [fa icon="long-arrow-right"]

Chatbots and Customer Service: Why Do Chatbots Fail?

[fa icon="calendar'] May 30, 2019 6:25:00 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

[fa icon="comment"] 0 Comments

Although chatbots have become quite popular in recent years, there is still room for improvement. A well-trained chatbot must correctly react to any query sent by a user creating a successful human-like conversation. Is that happening? We don’t think so.


Round-the-clock service, cost reduction and delivering a better customer experience are, among others, the main benefits of chatbots for customer service automation. If you are thinking of setting up a conversational agent to take care of your customers, it’s all-important for you to know not only the bright side, but also the dark one. You just can't spin up a basic chatbot and expect it to work well.

 

Read More [fa icon="long-arrow-right"]

Natural Language Processing (NLP) vs. Machine Learning

[fa icon="calendar'] May 20, 2019 5:06:17 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, NLP for CX

[fa icon="comment"] 0 Comments

Two concepts, one mission: to make machines understand humans. Natural Language Processing (NLP) and Machine Learning (ML) are all the rage right now, but people tend to mix them up. In this post, there will be a distinction between these two different but complementary terms in the field of Artificial Intelligence.

Read More [fa icon="long-arrow-right"]

Main Challenges for Word Embeddings: Part II

[fa icon="calendar'] Dec 28, 2018 10:32:29 AM / by Bitext posted in NLP, Deep Linguistic Analysis, Phrase Extraction, POS tagging, NLP for Core

[fa icon="comment"] 0 Comments

A ‘word embeddings’ approach has been widely adopted for machine learning processes. While an extensive research has been carried out during these years to analyze all theoretical underpinnings of algorithms such as word2vec, GloVe or fastText, it is surprising that little has been done, in turn, to solve some of the more complex linguistic issues raised when getting down to business.

Read More [fa icon="long-arrow-right"]

Main Challenges for Word Embeddings: Part I

[fa icon="calendar'] Dec 28, 2018 10:27:48 AM / by Bitext posted in NLP, Deep Linguistic Analysis, Phrase Extraction, POS tagging, NLP for Core

[fa icon="comment"] 0 Comments

Machine learning algorithms require a great amount of numeric data to work properly. Real people, however, do not speak to bots using numbers, they communicate through the natural language. That’s the main reason why chatbot developers need to convert all these words into digits so that those virtual assistants can understand what users are saying. And here is where word embeddings come into play.

Read More [fa icon="long-arrow-right"]

Subscribe to email Updates