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        <title>Distributional Modeling on a Diet: One-shot Word Learning from Text Only</title>
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    <abstract>We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative.</abstract>
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%0 Conference Proceedings
%T Distributional Modeling on a Diet: One-shot Word Learning from Text Only
%A Wang, Su
%A Roller, Stephen
%A Erk, Katrin
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F wang-etal-2017-distributional
%X We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative.
%U https://aclanthology.org/I17-1021
%P 204-213

Markdown (Informal)

[Distributional Modeling on a Diet: One-shot Word Learning from Text Only](https://aclanthology.org/I17-1021) (Wang et al., IJCNLP 2017)

• Distributional Modeling on a Diet: One-shot Word Learning from Text Only (Wang et al., IJCNLP 2017)

ACL

• Su Wang, Stephen Roller, and Katrin Erk. 2017. Distributional Modeling on a Diet: One-shot Word Learning from Text Only. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 204–213, Taipei, Taiwan. Asian Federation of Natural Language Processing.