Natural Language Processing (NLP) is a branch of Artificial Intelligence that fills in the gap between computers and human language. With the help of NLP, computers can read text, hear and interpret speech, measure emotions, determine contextual parts that are important, and operate with them.
Natural language processing requires different techniques to interpret human language as it is both voice- and text- driven. NLP breaks down the language into small elemental pieces, explores relationships between these pieces, and how these pieces work together to create meaning.
NLP does not work alone. It cooperates with:
Text mining is also a technology that belongs to artificial intelligence and uses natural language processing to transform the unstructured text in documents and databases into structured data that can be analyzed or applied to machine learning algorithms. It is broadly used in knowledge-driven organizations, where it is essential to examine large collections of documents, discover new information, or help answer specific research questions. Here, text mining identifies facts, relationships, and assertions that are buried in big data. The extracted information becomes structured and either analyzed further or presented directly using clustered HTML tables, mind maps, charts, etc. The structured data created by text mining can be used for descriptive, prescriptive, or predictive analytics by integrating it into databases, data warehouses, or business intelligence dashboards.
Machine learning is a technology of artificial intelligence that gives systems the ability to learn automatically from experience and solve complex problems with accuracy.
Machine learning trains perfectly only on the well-curated input, which cannot be extracted from unstructured text sources such as electronic health records (EHRs) or scientific literature. Thus, machine learning needs NLP to extract data from EHRs, clinical trial records, or full-text literature in a clean, structured way.