Named Entity Recognition in Biomedical Literature: A Comparison of Support Vector Machines and Conditional Random Fields

2008 ◽  
pp. 137-147
Author(s):  
Feng Liu ◽  
Yifei Chen ◽  
Bernard Manderick
Keyword(s):  
Support Vector Machines ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Support Vector ◽  
Named Entity ◽  
Vector Machines

scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

scholarly journals NAMED ENTITY RECOGNITION IN GREEK TEXTS WITH AN ENSEMBLE OF SVMS AND ACTIVE LEARNING

2007 ◽  
Vol 16 (06) ◽  
pp. 1015-1045 ◽  
Author(s):  
GIORGIO LUCARELLI ◽  
XENOFON VASILAKOS ◽  
ION ANDROUTSOPOULOS
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Support Vector ◽  
Named Entity ◽  
Input Text ◽  
Vector Machines ◽  
Temporal Expressions ◽  
General Collection

We present a freely available named-entity recognizer for Greek texts that identifies temporal expressions, person, and organization names. For temporal expressions, it relies on semi-automatically produced patterns. For person and organization names, it employs an ensemble of Support Vector Machines that scan the input text in two passes. The ensemble is trained using active learning, whereby the system itself proposes candidate training instances to be annotated by a human during training. The recognizer was evaluated on both a general collection of newspaper articles and a more focussed, in terms of topics, collection of financial articles.

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