Global Big Data Conference

Deep learning has yielded some fantastic results for basic natural language processing (NLP) functions such as named entity recognition (NER), document classification and sentiment analysis -- not to mention the abilities to generate everything from believable short stories to HTML code with minimal text inputs or prompts. In addition, deep learning can also have a dramatic impact on F1 scores, which are used as a performance measure for precision and recall, and so vendors have started to throw much of their weight and resources behind what they see as a game-changing technology. But as the CEO of a company that's been doing NLP for well over 15 years, I don't believe that deep learning is always the answer -- especially from an economic standpoint. I've watched as many new players have stepped up to the plate with NLP solutions underpinned by deep learning. But what I'm not seeing is evidence of big commercial wins, and I suspect that the cost of using deep learning-backed NLP is wiping out significant dollar gains. Deep learning tools like BERT can deliver results, but sometimes at a much greater cost than taking a traditional machine learning approach, depending on the size of your project.