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EMNLP
2009
13 years 5 months ago
Domain adaptive bootstrapping for named entity recognition
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retr...
Dan Wu, Wee Sun Lee, Nan Ye, Hai Leong Chieu
ACL
2006
13 years 9 months ago
Exploiting Comparable Corpora and Bilingual Dictionaries for Cross-Language Text Categorization
Cross-language Text Categorization is the task of assigning semantic classes to documents written in a target language (e.g. English) while the system is trained using labeled doc...
Alfio Massimiliano Gliozzo, Carlo Strapparava
ACL
2008
13 years 9 months ago
Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition
We present a novel hierarchical prior structure for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task acr...
Andrew Arnold, Ramesh Nallapati, William W. Cohen
ICASSP
2010
IEEE
13 years 7 months ago
Language model adaptation using Random Forests
In this paper we investigate random forest based language model adaptation. Large amounts of out-of-domain data are used to grow the decision trees while very small amounts of in-...
Anoop Deoras, Frederick Jelinek, Yi Su
ACL
2007
13 years 9 months ago
Instance Weighting for Domain Adaptation in NLP
Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation prob...
Jing Jiang, ChengXiang Zhai