Radu Florian, John C. Henderson, Grace Ngai
Transformation-based learning has been successfully employed to solve many natural language processing problems. It has many positive features, but one drawback is that it does not provide estimates...
This paper describes and extensively evaluates a sys-tem for the automatic routing of submitted papers to reviewers and area committees, without the need for any human annotation from the reviewers...
HowtogetaChineseName(Entity): Segmentation and Combination Issues (2007)
Jing, Hongyan, Florian, Radu, Luo, Xiaoqiang, Zhang, Tong, Ittycheriah, Abraham
When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters or words. While there is no unique...
Named Entity Recognition as a House of Cards: Classifier Stacking (2007)
This paper presents a classifier stacking-based approach to the named entity recognition task (NER henceforth). Transformation-based learning (Brill, 1995), Snow (sparse network of winnows (Mu oz et...
Factorizing complex models: A case study in mention detection (2006)
Radu Florian, Hongyan Jing, A Kambhatla, Imed Zitouni
As natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristics, more linguistic...
The Impact of Morphological Stemming on Arabic Mention Detection and Coreference Resolution (2005)
Imed Zitouni, Jeff Sorensen, Xiaoqiang Luo, Radu Florian
Arabic presents an interesting challenge to natural language processing, being a highly inflected and agglutinative language. In particular, this paper presents an in-depth investigation of the...
Word selection for ebmt based on monolingual similarity and translation (2003)
Shazia Akhtar, Ronan G. Reilly, Mari Ostendorf, Yaser Al-onaizan, Radu Florian, ...
[4] Hiyan Alshawi. Effective utterance classification with unsupervised phonotactic
Named entity recognition through classifier combination (2003)
Radu Florian, Abe Ittycheriah, Hongyan Jing, Tong Zhang
This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based...
Named entity recognition through classifier combination (2003)
Radu Florian, Abe Ittycheriah, Hongyan Jing, Tong Zhang
This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based...
HowtogetaChineseName(Entity): Segmentation and combination issues (2003)
Hongyan Jing, Radu Florian, Xiaoqiang Luo, Tong Zhang, Abraham Ittycheriah
hjing,raduf,xiaoluo,tzhang,abei¡ When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters...
Named entity recognition through classifier combination (2003)
Radu Florian, Abe Ittycheriah, Hongyan Jing, Tong Zhang
This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based...
Thesis (Ph. D.)--Johns Hopkins University, 2003.
Vita.
Thesis (Ph. D.)--Johns Hopkins University, 2003.
Named entity recognition as a house of cards: Classifier stacking (2002)
This paper presents a classifier stacking-based approach to the named entity recognition task (NER henceforth). Transformation-based learning (Brill,
Modeling consensus: Classifier combination for word sense disambiguation (2002)
This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including...
2002. Unsupervised italian word sense disambiguation using wordnets and unlabeled corpora (2002)
Unlabeled Corpora, Radu Florian, Richard Wicentowski
This paper presents a novel method for unsupervised word sense disambiguation, which combines multiple information sources, including semantic relations, large unlabeled corpora, and cross-lingual...
Named Entity Recognition as a House of Cards: Classifier Stacking (2002)
This paper presents a classifier stacking-based approach to the named entity recognition task (NER henceforth). Transformation-based learning (Brill, 1995), Snow (sparse network of winnows (Muoz et...
Combining classifiers for word sense disambiguation (2002)
Radu Florian, Silviu Cucerzan, Charles Schafer
Classifier combination is an e#ective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier...
Transformation-Based Learning in the Fast Lane (2001)
Transformation-based learning has been successfully employed to solve many natural language processing problems. It achieves state-of-the-art performance on many natural language processing tasks and...
Multidimensional Transformation-Based Learning (2001)
This paper presents a novel method that allows a machine learning algorithm following the transformation-based learning paradigm \cite{brill95:tagging} to be applied to multiple classification tasks...
Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation (2001)
Florian, Radu, Yarowsky, David
This paper presents a novel method of generating and applying hierarchical, dynamic topic-based language models. It proposes and evaluates new cluster generation, hierarchical smoothing and adaptive...
Florian, Radu, Henderson, John C., Ngai, Grace
Transformation-based learning has been successfully employed to solve many natural language processing problems. It has many positive features, but one drawback is that it does not provide estimates...
Multidimensional transformation-based learning (2001)
This paper presents a novel method that allows a machine learning algorithm following the transformation-based learning paradigm (Brill, 1995) to be applied to multiple classication tasks by training...
The Johns Hopkins SENSEVAL2 system descriptions (2001)
David Yarowsky, Silviu Cucerzan, Radu Florian, Charles Schafer, Richard Wicentowski
This article describes the Johns Hopkins University (JHU) sense-disambiguation systems that participated in seven SENSEVAL2 tasks: four supervised lexical choice systems (Basque, English, Spanish,...
Transformation-based learning in the fast lane (2001)
Transformation-based learning has been successfully employed to solve many natural language processing problems. It achieves state-of-the-art performance on many natural language processing tasks and...
Radu Florian, John C. Henderson, Grace Ngai
Transformation-based learning has been success-fully employed to solve many natural language processing problems. It has many positive fea-tures, but one drawback is that it does not provide...
Dynamic nonlocal language modeling via hierarchical topic-based adaptation (1999)
This paper presents a novel method of generating and applying hierarchical, dynamic topic-based lan-guage models. It proposes and evaluates new clus-ter generation, hierarchical smoothing and...
Taking the Load Off the Conference Chairs: Towards a Digital Paper-Routing Assistant (1999)
This paper describes and extensively evaluates a system for the automatic routing of submitted papers to reviewers and area committees, without the need for any human annotation from the reviewers or...
Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation (1999)
This paper presents a novel method of generating and applying hierarchical, dynamic topic-based language models. It proposes and evaluates new cluster generation, hierarchical smoothing and adaptive...
Beyond n-grams: Can linguistic sophistication improve language modeling (1998)
Eric Brill, Radu Florian, John C. Henderson, Lidia Mangu
It seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. Interestingly, state of the art language models for speech...
Beyond n-grams: Can linguistic sophistication improve language modeling (1998)
Eric Brill, Radu Florian, John C. Henderson, Lidia Mangu
It seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. Interestingly, state of the art language models for speech...
Transformation Based Parsing (1998)
In this report we address the parsing problem, a well studied problem in the computational linguistics community. Parsing oers the base for more sophisticated processing, such as lexical analysis and...
Beyond N-Grams: Can Linguistic Sophistication Improve Language Modeling? (1998)
Eric Brill Radu, Radu Florian, John C. Henderson, Lidia Mangu
It seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. Interestingly, state of the art language models for speech...
Beyond N-Grams: Can Linguistic Sophistication Improve Language Modeling? (1998)
Eric Brill, Radu Florian, John C. Henderson, Lidia Mangu
It seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. Interestingly, state of the art language models for speech...
Beyond N-Grams: Can Linguistic Sophistication Improve Language Modeling? (1998)
Eric Brill, Radu Florian, John C. Henderson, Lidia Mangu
It seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. Interestingly, state of the art language models for speech...
Beyond n-grams: Can linguistic sophistication improve language modeling (1998)
Eric Brill, Radu Florian, John C. Henderson, Lidia Mangu
It seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. Interestingly, state of the art language models for speech...
Language models are used in various elds that deal with sequences of words. We present in this paper automatic methods to cluster sets of documents into topic trees (that go from general to specic)....