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Natural Language Generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations. It could be said an NLG system is like a translator that converts a computer based representation into a natural language representation. However, the methods to produce the final language are different from those of a compiler due to the inherent expressivity of natural languages. NLG has existed for a long time but it is only recently that commercial NLG technology had become widely available and self service. NLG may be viewed as the opposite of natural language understanding: whereas in natural language understanding the system needs to disambiguate the input sentence to produce the machine representation language, in NLG the system needs to make decisions about how to put a concept into words. Simple examples are systems that generate form letters. These do not typically involve grammar rules, but may generate a letter to a consumer, e.g. stating that a credit card spending limit was reached. More complex NLG systems dynamically create texts to meet a communicative goal. As in other areas of natural language processing, this can be done using either explicit models of language (e.g., grammars) and the domain, or using statistical models derived by analysing human-written texts. ==Example== The ''Pollen Forecast for Scotland'' system 〔R Turner, S Sripada, E Reiter, I Davy (2006). (Generating Spatio-Temporal Descriptions in Pollen Forecasts. ) ''Proceedings of EACL06''〕 is a simple example of an NLG system. This system takes as input six numbers, which give predicted pollen levels in different parts of Scotland. From these numbers, the system generates a short textual summary of pollen levels as its output. For example, using the historical data for 1-July-2005, the software produces
In contrast, the actual forecast (written by a human meteorologist) from this data was
Comparing these two illustrates some of the choices that NLG systems must make; these are further discussed below. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Natural language generation」の詳細全文を読む スポンサード リンク
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