Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
ME-based biomedical named entity recognition using lexical knowledge
ACM Transactions on Asian Language Information Processing (TALIP)
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Feature selection techniques for maximum entropy based biomedical named entity recognition
Journal of Biomedical Informatics
Two-phase biomedical named entity recognition using a hybrid method
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Biomedical named entity recognition: a poor knowledge HMM-based approach
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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Entity extraction is one of the most fundamental and important tasks in biomedical information extraction. In this paper we propose a two-stage algorithm for the extraction of biomedical entities in the forms of genes and gene product mentions in text. Several different approaches have emerged but most of these state-of-the-art approaches suggest that individual system may not cover entity representations with arbitrary set of features and cannot achieve best performance. We identify and implement a diverse set of features which are relevant for the identification of biomedical entities and classification of them into some predefined categories. One most important criterion of these features is that these are identified and selected largely without using any domain knowledge. In the first stage we use a genetic algorithm (GA) based feature selection technique to determine the most relevant set of features for Support Vector Machine (SVM) and Conditional Random Field (CRF) classifiers. The GA based feature selection algorithm produces best population that can be used to generate different classification models based on CRF and SVM. In the second stage we develop a stacked based ensemble to combine the classifiers selected in the first stage. The proposed approach is evaluated on two benchmark datasets, namely JNLPBA 2004 shared task and GENETAG. The proposed approach yields the overall F-measure values of 75.17% and 94.70% for JNLPBA 2004 and GENETAG data sets, respectively.