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Natural language understanding
 | This article needs to be updated. The reason given is: lack of discussion of recent developments related to large language models, but also no mention of older techniques like word embedding or word2vec. (February 2024) |
Natural language understanding (NLU) or natural language interpretation (NLI)[1] is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.[2]
There is considerable commercial interest in the field because of its application to automated reasoning,[3] machine translation,[4] question answering,[5] news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.
- ^ Semaan, P. (2012). Natural Language Generation: An Overview. Journal of Computer Science & Research (JCSCR)-ISSN, 50-57
- ^ Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness . In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) --In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3-17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf
- ^ Van Harmelen, Frank, Vladimir Lifschitz, and Bruce Porter, eds. Handbook of knowledge representation. Vol. 1. Elsevier, 2008.
- ^ Macherey, Klaus, Franz Josef Och, and Hermann Ney. "Natural language understanding using statistical machine translation." Seventh European Conference on Speech Communication and Technology. 2001.
- ^ Hirschman, Lynette, and Robert Gaizauskas. "Natural language question answering: the view from here." natural language engineering 7.4 (2001): 275-300.
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