Tuning support vector machines for biomedical named entity recognition

Kazama, Jun'ichi; Makino, Takaki; Ohta, Yoshihiro; Tsujii, Jun’ichi

doi:10.3115/1118149.1118150

Cited by 183 publications

(156 citation statements)

References 15 publications

(13 reference statements)

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“…In this regards, few researchers have proposed the utilization of named entity in their own particular way. The authors [13], [17] has gathered most common prefix and suffix from training data. Whilst [12], [18] the author gathered 23 categories of prefix and suffix data using statistical methods as their own distribution.…”

Section: ) Affixesmentioning

confidence: 99%

Bio-NER: Biomedical Named Entity Recognition using Rule-Based and Statistical Learners

Soomro¹,

Kumar²,

Banbhrani³

et al. 2017

ijacsa

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Section: ) Affixesmentioning

confidence: 99%

Bio-NER: Biomedical Named Entity Recognition using Rule-Based and Statistical Learners

Soomro¹,

Kumar²,

Banbhrani³

et al. 2017

ijacsa

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“…Kazama et al [25] used SVMs to identify proteins, DNA, cell types, cell lines and lipids, with a F-measure of 73.6%. Tsai et al [26] constructed a NER framework with CRF.…”

Section: The Machine-learning Ner Approachmentioning

confidence: 99%

A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining

Sun¹,

Liu²,

Cai³

et al. 2017

Preprint

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At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

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“…The second study contrasts two models in the combined identification and classification task (Kazama et al, 2002). Word frequency, part-of-speech tags, inflectional morphology and lexical inclusion are used as input to a SVM and Maximum Entropy (ME) model.…”

Section: Related Workmentioning

confidence: 99%

Using distributional similarity to organise biomedical terminology

Weeds

Dowdall

Schneider

et al. 2005

TERM

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We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are defined for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of different measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy.

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Tuning support vector machines for biomedical named entity recognition

Cited by 183 publications

References 15 publications

Bio-NER: Biomedical Named Entity Recognition using Rule-Based and Statistical Learners

Bio-NER: Biomedical Named Entity Recognition using Rule-Based and Statistical Learners

A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining

Using distributional similarity to organise biomedical terminology

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