Learning the Central Events and Participants in Unlabeled Text
Abstract
The majority of information on the Internet is expressed in written text. Understanding and extracting this information is crucial to building intelligent systems that can organize this knowledge. Today, most algorithms focus on learning atomic facts and relations. For instance, we can reliably extract facts like Annapolis is a City by observing redundant word patterns across a corpus. However, these facts do not capture richer knowledge like the way detonating a bomb is related to destroying a building, or that the perpetrator who was convicted must have been arrested. A structured model of these events and entities is needed for a deeper understanding of language. This talk describes unsupervised approaches to learning such rich knowledge.
Cite
Text
Chambers and Jurafsky. "Learning the Central Events and Participants in Unlabeled Text." International Conference on Machine Learning, 2012.Markdown
[Chambers and Jurafsky. "Learning the Central Events and Participants in Unlabeled Text." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/chambers2012icml-learning/)BibTeX
@inproceedings{chambers2012icml-learning,
title = {{Learning the Central Events and Participants in Unlabeled Text}},
author = {Chambers, Nathanael and Jurafsky, Dan},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/chambers2012icml-learning/}
}