Towards Understanding Situated Natural Language
Abstract
We present a general framework and learning algorithm for the task of concept labeling: each word in a given sentence has to be tagged with the unique physical entity (e.g. person, object or location) or abstract concept it refers to. Our method allows both world knowledge and linguistic information to be used during learning and prediction. We show experimentally that we can learn to use world knowledge to resolve ambiguities in language, such as word senses or reference resolution, without the use of handcrafted rules or features.
Cite
Text
Bordes et al. "Towards Understanding Situated Natural Language." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Bordes et al. "Towards Understanding Situated Natural Language." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/bordes2010aistats-understanding/)BibTeX
@inproceedings{bordes2010aistats-understanding,
title = {{Towards Understanding Situated Natural Language}},
author = {Bordes, Antoine and Usunier, Nicolas and Collobert, Ronan and Weston, Jason},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {65-72},
volume = {9},
url = {https://mlanthology.org/aistats/2010/bordes2010aistats-understanding/}
}