Results 1 - 10 of 80

BANNER: An executable survey of advances in biomedical named entity recognition

by Robert Leaman, Graciela Gonzalez - In Pac Symp Biocomput , 2008
"... There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances ..."
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the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields

The impact of near domain transfer on biomedical named entity recognition

by Nigel Collier, Ferdinand Paster, Mai-vu Tran
"... Current research in fully supervised biomedical named entity recognition (bioNER) is often conducted in a setting of low sample sizes. Whilst experi-mental results show strong performance in-domain it has been recognised that quality suffers when models are applied to heterogeneous text collections. ..."
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Current research in fully supervised biomedical named entity recognition (bioNER) is often conducted in a setting of low sample sizes. Whilst experi-mental results show strong performance in-domain it has been recognised that quality suffers when models are applied to heterogeneous text collections

Tuning Support Vector Machines for Biomedical Named Entity Recognition

by Jun'ichi Kazama, Takaki Makino, Yoshihiro Ohta, Jun'ichi Tsujii - In Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain , 2002
"... We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus -- the GENIA corpus -- tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition, we expl ..."
Abstract - Cited by 89 (6 self) - Add to MetaCart
We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus -- the GENIA corpus -- tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition, we

Named Entity Recognition in Various Biomedical Domain

by Hyun-sook Lee, Hyunchul Jang, Jaesoo Lim
"... Until now, research in named entity (NE) recognition from bio-medical literature has focused on a limited domain. Most of biological NE recognition systems are designed to extract fixed semantic types of NEs and need domain-specific knowledge either by dictionaries and rules[1][2] or by some supervi ..."
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Until now, research in named entity (NE) recognition from bio-medical literature has focused on a limited domain. Most of biological NE recognition systems are designed to extract fixed semantic types of NEs and need domain-specific knowledge either by dictionaries and rules[1][2] or by some

Y.: Biomedical named entity recognition system

by Jon Patrick, Yefeng Wang - In: Proceedings of the Tenth Australasian Document Computing Symposium (ADCS , 2005
"... Abstract We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class label ..."
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Abstract We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class

Named Entity Recognition: Adapting to Microblogging

by Brian Locke, Dr. James Martin Ph. D
"... In this project, we seek to create a Named Entity Recognizer (NER) tuned for use on Twitter posts. We will be identifying Named Entities and classifying them as People, Locations, or Organizations. We hope to identify language features and methods that effectively transfer the techniques and knowled ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
In this project, we seek to create a Named Entity Recognizer (NER) tuned for use on Twitter posts. We will be identifying Named Entities and classifying them as People, Locations, or Organizations. We hope to identify language features and methods that effectively transfer the techniques

A Maximum Entropy Approach to Biomedical Named Entity Recognition

by Yi-feng Lin, Tzong-han Tsai, Wen-chi Chou, Kuen-pin Wu, Ting-yi Sung, Wen-lian Hsu - Proceedings of KDD Workshop on Data Mining and Bioinformatics , 2004
"... Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underlying machine learning method incorporated with dictionary-based and rule-based methods for post-processing. Simply using M ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underlying machine learning method incorporated with dictionary-based and rule-based methods for post-processing. Simply using

Exploiting feature hierarchy for transfer learning in named entity recognition

by Andrew Arnold, Ramesh Nallapati, William W. Cohen - In ACL:HLT ’08 , 2008
"... 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 across natural language data sets. The problem of transfer learning, where information gained in one learning task is used t ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
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 across natural language data sets. The problem of transfer learning, where information gained in one learning task is used

Two-phase biomedical named entity recognition using a hybrid method

by Seonho Kim, Kyung-mi Park - In Proceedings of The Second International Joint Conference on Natural Language Processing (IJCNLP-05 , 2005
"... Abstract. Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical variations. This paper presents a two-phase biomedical NER consisting of term boundary detection and semantic ..."
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Abstract. Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical variations. This paper presents a two-phase biomedical NER consisting of term boundary detection and semantic

Tuning Support Vector Machines for Biomedical Named Entity Recognition

by Jun'ichi Kazama Takaki, Takaki Makino, Yoshihiro Ohta - In Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain , 2002
"... We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus -- the GENIA corpus -- tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition, ..."
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We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus -- the GENIA corpus -- tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition
Results 1 - 10 of 80