natural language processing
Artificial intelligence and machine learning becomes more pervasive, the way we live and work is being fundamentally altered. Embedding the “finely-tuned” predictive outcomes from machine learning into business processes can be a game-changer especially where modern and core applications are integrated to optimize and, in some cases, automate decision making. Furthermore, deep learning has shown that the right algorithms can surpass humans in scale and speed in the areas of natural language processing and image recognition.
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI). The development of NLP applications is challenging because computers traditionally require humans to "speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. Human speech, however, is not always precise it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.
Most of the research being done on natural language processing revolves around search, especially enterprise search. This involves allowing users to query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, such as those that might correspond to specific features in a data set, and returns an answer.
NLP can be used to interpret free text and make it analyzable. There is a tremendous amount of information stored in free text files. For example, patients google reviews for doctors. Prior to deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any kind of systematic way. But NLP allows analysts to sift through massive troves of free text to find relevant information in the files.