Sentence generation for artificial brains: A glocal similarity-matching approach

@article{Lian2010SentenceGF,
  title={Sentence generation for artificial brains: A glocal similarity-matching approach},
  author={Ruiting Lian and Ben Goertzel and Rui Liu and Michael Ross and Murilo Saraiva de Queiroz and Linas Vepstas},
  journal={Neurocomputing},
  year={2010},
  volume={74},
  pages={95-103}
}
A novel approach to sentence generation – SegSim, Sentence Generation by Similarity Matching – is outlined, and is argued to possess a number of desirable properties making it plausible as a model of sentence generation in the human brain, and useful as a guide for creating sentence generation components within artificial brains. The crux of the approach is to do as much as possible via similarity matching against a large knowledge base of previously comprehended sentences, rather than via… CONTINUE READING

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