Prediction and Semantic Association
Abstract
We explore the consequences of viewing semantic association as the result of attempting to predict the concepts likely to arise in a particular context. We argue that the success of existing accounts of semantic representation comes as a result of indirectly addressing this problem, and show that a closer correspondence to human data can be obtained by taking a probabilistic approach that explicitly models the generative structure of language.
Cite
Text
Griffiths and Steyvers. "Prediction and Semantic Association." Neural Information Processing Systems, 2002.Markdown
[Griffiths and Steyvers. "Prediction and Semantic Association." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/griffiths2002neurips-prediction/)BibTeX
@inproceedings{griffiths2002neurips-prediction,
title = {{Prediction and Semantic Association}},
author = {Griffiths, Thomas L. and Steyvers, Mark},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {11-18},
url = {https://mlanthology.org/neurips/2002/griffiths2002neurips-prediction/}
}