Natural language processing

Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics. It studies the problems inherent in the processing and manipulation of natural language, but not, generally, natural language understanding.

The major tasks in NLP are:

Text to speech
Speech recognition
Natural language generation
Machine translation
Question answering
Information retrieval
Information extraction
Text-proofing
Translation technology
Text Summaries

Some problems which make NLP difficult:

Word boundary detection: In spoken language, there are no gaps between words; where to place the word boundary often depends on what choice makes the most sense grammatically and given the context. In written form, languages like Chinese do not have word boundaries either.
Word sense disambiguation: Any given word can have several different meanings; we have to select the meaning which makes the most sense in context.
Syntactic ambiguity: The grammar for natural languages is not unambiguous, i.e. there are often multiple possible parse trees for a given sentence. Choosing the most appropriate one usually requires semantic and contextual information.
Imperfect or irregular input: Foreign or regional accents and vocal impediments in speech; typing or grammatical errors, OCR errors in texts.
Speech acts and plans: Sentences often don't mean what they literally say; for instance a good answer to "Can you pass the salt" is to pass the salt; in most contexts "Yes" is not a good answer, although "No" is better and "I'm afraid that I can't see it" is better yet. Or again, if a class was not offered last year, "The class was not offered last year" is a better answer to the question "How many students failed the class last year?" than "None" is.

Table of contents

Statistical NLP

Statistical natural language processing uses stochastic methods to solve some of the problems discussed above, notably the ambiguity problems. These methods often involve the use of corpora and Markov models.