Natural language processing (NLP) is the ability to extract insights from and literally understand natural language within text, audio and images. Language and text hold huge insight, and that data is often prevalent and widespread in many organizations. The ability to process language systematically, effectively and at scale lends itself to numerous applications across almost any organization with application to customer-facing products and services and customer support through to big process changes in the back office. NLP applications can apply to speech-to-text, text-to-speech, language translation, language classification and categorization, named entity recognition, language generation, automatic summarization, similarity assessment, language logic and consistency, and more.
How NLP and BERT will change the language game
Why deep learning for NLP? One word: BERT.
There has been a range of techniques applied to NLP, but deep learning, in particular, is showing some exceptional results. Deep learning has been applied to NLP for several years, and research and development breaks new ground so quickly that new methods and increasingly capable models are rapidly occurring. For example, FastText, an extension of Word2Vec, now significantly reduces the training load, which makes the application to a specific data set and language context much easier.
But a recent model open-sourced by Google in October 2018, BERT (Bidirectional Encoder Representations from Transformers, is now reshaping the NLP landscape. BERT is significantly more evolved in its understanding of word semantics given its context and has an ability to process large amounts of text and language. BERT also makes it easier to reuse a pre-trained model (transfer learning) and then fine-tune your data and the specific language situation and problem you face.
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