Natural language processing
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, like patients’ medical records, for example. Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way. But NLP allows analysts to sift through massive troves of free text to find relevant information in the files.
Google and other search engines base their machine translation technology on NLP deep learning models. This allows algorithms to read text on a webpage, interpret its meaning and translate it to another language.
The advantage of natural language processing can be seen when considering the following two statements: “Cloud computing insurance should be part of every service level agreement” and “A good SLA ensures an easier night’s sleep — even in the cloud.” If you use natural language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service level agreement.
These are the types of vague elements that frequently appear in human language and that machine learning algorithms have historically been bad at interpreting. Now, with improvements in deep learning and artificial intelligence, algorithms can effectively interpret them.
This has implications for the types of data that can be analyzed. More and more information is being created online every day, and a lot of it is natural human language. Until recently, businesses have been unable to analyze this data. But advances in NLP make it possible to analyze and learn from a greater range of data sources.
NLP hosts benefits such as:
Improved accuracy and efficiency of documentation.
The ability to automatically make a readable summary text.
Useful for personal assistants such as Alexa.
Allows an organization to use chatbots for customer support.
Easier to perform sentiment analysis.