On the Recursive Neural Networks for Relation Extraction and Entity Recognition

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https://hdl.handle.net/2142/46992

Title: On the Recursive Neural Networks for Relation Extraction and Entity Recognition
Author(s): Khashabi, Daniel
Contributor(s): Roth, Dan
Issue Date: 2013-05-01
Keyword(s): Neural Networks, Relation Extraction, Entity Recognition, Compositionality, Natural Language, Machine Learning
Date of Ingest: 2014-01-18T17:49:36Z
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.
Type of Resource: text
Genre of Resource: Article
Language: en
Permalink: http://hdl.handle.net/2142/46992