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Title:On the Recursive Neural Networks for Relation Extraction and Entity Recognition
Author(s):Khashabi, Daniel
Contributor(s):Roth, Dan
Subject(s):Neural Networks, Relation Extraction, Entity Recognition, Compositionality, Natural Language, Machine Learning
Abstract:Recently there has been a surge of interest in neural architectures for complex structured learning tasks. Along this track, we are ad-dressing the supervised task of relation extrac-tion and named-entity recognition via recur-sive neural structures and deep unsupervised feature learning. Our models are inspired by several recent works in deep learning for nat-ural language. We have extended the pre-vious models, and evaluated them in various scenarios, for relation extraction and named-entity recognition. In the models, we avoid using any external features, so as to inves-tigate the power of representation instead of feature engineering. We implement the mod-els and proposed some more general models for future work. We will briefly review pre-vious works on deep learning and give a brief overview of recent progresses relation extrac-tion and named-entity recognition.
Issue Date:2013-05-01
Genre:Article
Type:Text
Language:English
URI:http://hdl.handle.net/2142/46992
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Date Available in IDEALS:2014-01-18


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