Natural language processing - artificial intelligence

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages so far. As such, NLP is related to the area of human-computer interaction. At the movement Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.

NLP research has been applied to a wide variety of tasks, including machine translation, information retrieval, question answering, text summarization, sentiment analysis, dialogue systems, and speech recognition. The research is also often divided into subfields, such as computational linguistics, psycholinguistics, neurolinguistics, and pragmatics.

NLP is a complex field, and there is no single approach to solving NLP problems. Instead, NLP researchers often develop and use a variety of techniques, including rule-based systems, statistical methods, and machine learning.

Rule-based systems are often used for tasks such as part-of-speech tagging and named entity recognition. These systems use a set of rules to determine how to label each word in a sentence. For example, a rule-based part-of-speech tagger might use rules to determine that the word "cat" is a noun, while the word "dog" is a verb.

Statistical methods are often used for tasks such as machine translation and speech recognition. These methods use statistical models to determine the likelihood that a given word or phrase will appear in a particular context. For example, a statistical machine translation system might use a statistical model to determine the likelihood that the French word "chat" will be translated as the English word "cat."

Machine learning is a subfield of artificial intelligence that is concerned with the design and development of algorithms that can learn from data. Machine learning is often used for tasks such as text classification and sentiment analysis. For example, a machine learning algorithm might be used to learn the rules for part-of-speech tagging from a training set of labeled data.

NLP is a complex and interdisciplinary field, and there is no single approach to solving NLP problems. NLP researchers often use a variety of techniques, including rule-based systems, statistical methods, and machine learning.