Rule-based natural language processing

Rule-based natural language processing (NLP) is a type of NLP that uses a set of hand-crafted rules to extract information from text. This type of NLP is often used in applications where accuracy is critical, such as medical diagnosis and legal research.

Rule-based NLP systems typically consist of two main components: a knowledge base and a rule engine. The knowledge base contains a set of facts about the world, such as the names of people, places, and things. The rule engine uses these facts to generate rules that can be used to extract information from text.

For example, a rule-based NLP system might have a rule that states that if the word "doctor" appears in a sentence, then the sentence is likely about a medical topic. This rule could be used to extract information about doctors from medical documents.

Rule-based NLP systems are typically more accurate than machine learning-based NLP systems, but they are also more difficult to develop and maintain. This is because the rules must be carefully crafted to reflect the nuances of human language.

Despite the challenges, rule-based NLP systems are still a valuable tool for extracting information from text. They are especially useful in applications where accuracy is critical, such as medical diagnosis and legal research.