Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition. Arnold, A., Nallapati, R., & Cohen, W., W. Proceedings of ACL08 HLT, Association for Computational Linguistics, 2008. ![Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex We present a novel hierarchical prior struc- ture for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language data sets. The problem of transfer learning, where information gained in one learning task is used to improve perfor- mance in another related task, is an important new area of research. In the subproblem of do- main adaptation, a model trained over a source domain is generalized to perform well on a re- lated target domain, where the two domains’ data are distributed similarly, but not identi- cally. We introduce the concept of groups of closely-related domains, called genres, and show how inter-genre adaptation is related to domain adaptation. We also examine multi- task learning, where two domains may be re- lated, but where the concept to be learned in each case is distinct. We show that our prior conveys useful information across domains, genres and tasks, while remaining robust to spurious signals not related to the target do- main and concept. We further show that our model generalizes a class of similar hierarchi- cal priors, smoothed to varying degrees, and lay the groundwork for future exploration in this area.
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title = {Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition},
type = {article},
year = {2008},
pages = {245-253},
websites = {http://www.aclweb.org/anthology/P/P08/P08-1029},
publisher = {Association for Computational Linguistics},
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created = {2012-02-28T00:52:49.000Z},
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last_modified = {2017-03-14T14:36:19.698Z},
tags = {named entity recognition},
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abstract = {We present a novel hierarchical prior struc- ture for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language data sets. The problem of transfer learning, where information gained in one learning task is used to improve perfor- mance in another related task, is an important new area of research. In the subproblem of do- main adaptation, a model trained over a source domain is generalized to perform well on a re- lated target domain, where the two domains’ data are distributed similarly, but not identi- cally. We introduce the concept of groups of closely-related domains, called genres, and show how inter-genre adaptation is related to domain adaptation. We also examine multi- task learning, where two domains may be re- lated, but where the concept to be learned in each case is distinct. We show that our prior conveys useful information across domains, genres and tasks, while remaining robust to spurious signals not related to the target do- main and concept. We further show that our model generalizes a class of similar hierarchi- cal priors, smoothed to varying degrees, and lay the groundwork for future exploration in this area.},
bibtype = {article},
author = {Arnold, Andrew and Nallapati, Ramesh and Cohen, William W},
journal = {Proceedings of ACL08 HLT},
number = {June}
}
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