Incorporating rich background knowledge for gene named entity classification and recognition. Li, Y., Lin, H., & Yang, Z. BMC Bioinformatics, 10(1):223, BioMed Central, 2009. ![Incorporating rich background knowledge for gene named entity classification and recognition [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper ![Incorporating rich background knowledge for gene named entity classification and recognition [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex Background: Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information. Results: We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at .
@article{
title = {Incorporating rich background knowledge for gene named entity classification and recognition},
type = {article},
year = {2009},
pages = {223},
volume = {10},
websites = {http://www.biomedcentral.com/1471-2105/10/223},
publisher = {BioMed Central},
institution = {Department of Computer Science and Engineering, Dalian University of Technology, Dalian, PR China. liyanpeng.lyp@gmail.com},
id = {12f9f5df-b4fc-3839-9f24-905d22e65105},
created = {2012-01-21T12:35:31.000Z},
file_attached = {true},
profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
last_modified = {2017-03-14T14:36:19.698Z},
tags = {named entity recognition},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {Li2009m},
private_publication = {false},
abstract = {Background: Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information. Results: We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at .},
bibtype = {article},
author = {Li, Yanpeng and Lin, Hongfei and Yang, Zhihao},
journal = {BMC Bioinformatics},
number = {1}
}
Downloads: 0
{"_id":"nAqKLH2DuzMihX2CB","bibbaseid":"li-lin-yang-incorporatingrichbackgroundknowledgeforgenenamedentityclassificationandrecognition-2009","authorIDs":[],"author_short":["Li, Y.","Lin, H.","Yang, Z."],"bibdata":{"title":"Incorporating rich background knowledge for gene named entity classification and recognition","type":"article","year":"2009","pages":"223","volume":"10","websites":"http://www.biomedcentral.com/1471-2105/10/223","publisher":"BioMed Central","institution":"Department of Computer Science and Engineering, Dalian University of Technology, Dalian, PR China. liyanpeng.lyp@gmail.com","id":"12f9f5df-b4fc-3839-9f24-905d22e65105","created":"2012-01-21T12:35:31.000Z","file_attached":"true","profile_id":"5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6","group_id":"066b42c8-f712-3fc3-abb2-225c158d2704","last_modified":"2017-03-14T14:36:19.698Z","tags":"named entity recognition","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Li2009m","private_publication":false,"abstract":"Background: Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information. Results: We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at .","bibtype":"article","author":"Li, Yanpeng and Lin, Hongfei and Yang, Zhihao","journal":"BMC Bioinformatics","number":"1","bibtex":"@article{\n title = {Incorporating rich background knowledge for gene named entity classification and recognition},\n type = {article},\n year = {2009},\n pages = {223},\n volume = {10},\n websites = {http://www.biomedcentral.com/1471-2105/10/223},\n publisher = {BioMed Central},\n institution = {Department of Computer Science and Engineering, Dalian University of Technology, Dalian, PR China. liyanpeng.lyp@gmail.com},\n id = {12f9f5df-b4fc-3839-9f24-905d22e65105},\n created = {2012-01-21T12:35:31.000Z},\n file_attached = {true},\n profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},\n group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},\n last_modified = {2017-03-14T14:36:19.698Z},\n tags = {named entity recognition},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2009m},\n private_publication = {false},\n abstract = {Background: Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information. Results: We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at .},\n bibtype = {article},\n author = {Li, Yanpeng and Lin, Hongfei and Yang, Zhihao},\n journal = {BMC Bioinformatics},\n number = {1}\n}","author_short":["Li, Y.","Lin, H.","Yang, Z."],"urls":{"Paper":"https://bibbase.org/service/mendeley/bfdabac2-d7f2-3c5b-aa7a-06431c0ae35e/file/475c7a87-d963-ffdf-2faa-7d7603ce6fe7/2009-Incorporating_rich_background_knowledge_for_gene_named_entity_classification_and_recognition.pdf.pdf","Website":"http://www.biomedcentral.com/1471-2105/10/223"},"bibbaseid":"li-lin-yang-incorporatingrichbackgroundknowledgeforgenenamedentityclassificationandrecognition-2009","role":"author","downloads":0,"html":""},"bibtype":"article","creationDate":"2020-02-06T23:48:11.918Z","downloads":0,"keywords":[],"search_terms":["incorporating","rich","background","knowledge","gene","named","entity","classification","recognition","li","lin","yang"],"title":"Incorporating rich background knowledge for gene named entity classification and recognition","year":2009}