Science Fair Project Encyclopedia Natural language generation
Some people view NLG as the opposite of natural language
understanding. The difference can be put this way: 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 take decisions about how to
put a concept into words. The process to generate text can be as
simple as keeping a list of canned text that is copied and
pasted, possibly linked with some glue text. The results may be
satisfactory in simple domains such as horoscope machines or
generators of personalised business letters. However, a
sophisticated NLG system needs to include stages of planning and
merging of information to enable the generation of text that
looks natural and does not become repetitive. Typical stages
are: Content determination: Determination of the salient
features that are worth being said. Methods used in this stage
are related to data mining. Discourse plannin