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Abstract

We present a novel connectionist model for acquiring the semantics of language through the behavioral experiences of a real robot. We focus on the ``compositionality'' of semantics, which is a fundamental characteristic of human language, namely, the fact that we can understand the meaning of a sentence as a combination of the meanings of words. The essential claim is that a compositional semantic representation can be self-organized by generalizing correspondences between sentences and behavioral patterns. This claim is examined and confirmed through simple experiments in which a robot generates corresponding behaviors from unlearned sentences by analogy with the correspondences between learned sentences and behaviors.

@inproceedings{sugita04nips16,
  author={Y. Sugita and J. Tani},
  title={A Holistic Approach to Compositional Semantics: a connectionist model and robot experiments},
  year={2004},
  publisher={The MIT Press},
  booktitle={Advances in Neural Information Processing Systems 16},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/sugita04nips16.html}
}

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