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.
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.
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.
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.
Although Machine Learning algorithms have been around since mid-20th century, this technology along with Deep Learning is the newest popular boy in town, with good reason. Due to recent advances in computing power and data availability, they're being more and more used to perform astonishing tasks.