Natural language processing

Natural language processing is a subfield of artificial intelligence. It studies the problems inherent in the processing and manipulation of natural language, but not, generally, natural language understanding. It should not be confused with computational linguistics, which is in the domain of linguistics.

The major tasks in NLP are:

• Text to speech
• Speech recognition
• Natural language generation[?]
• Machine translation
• Full-text indexing
• Text-proofing

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.

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.

See also:

• the fictional universal translator