Abstract: A method and system for employing a Language Processing machine learning Artificial Intelligence engine to employ word embeddings to create numerical representations of document meaning in a high-dimensional semantic space or an overall semantic direction. A system and method configured to improve precision and scale when using simple static embeddings. The system is programmed to employ innovative algorithms that act as heuristics to eliminate bad contextual matches to improve accuracy and precision. Pretrained neural language models and contextual embeddings are configured automatically take context into account at the featurization stage. A system and method for one-class classification framework, which starts from a list of keywords of interest and is configured to build an intent/no intent classifier without any labels.