Integrating linguistic knowledge into a conditional random fieldframework to identify biomedical named entities

Tsai, Richard Tzong-Han; Chou, Wen Chi; Wu, Shih-Hung; Sung, Ting‐Yi; Hsiang, Jieh; Hsu, Wen-Lian

doi:10.1016/j.eswa.2005.09.072

Cited by 43 publications

(16 citation statements)

References 42 publications

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“…We use regular expressions to characterize orthographical features which are listed in Table 1. A part of them are also used by Tsai and Settles [33,34].…”

Section: Orthographical Featuresmentioning

confidence: 99%

Biomedical named entity recognition using generalized expectation criteria

Yao

Sun

et al. 2011

Int. J. Mach. Learn. & Cyber.

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It is difficult to apply machine learning to a domain which is short of labeled training data, such as biomedical named entity recognition (NER) which remains a challenging task because of its extraordinary complex nomenclature. In this paper, we proposed a semi-supervised method which can train condition random field (CRF) models using generalized expectation (GE) criteria to solve biomedical named entity recognition problem. In the proposed method, instead of ''instance'' labeling, the ''feature'' labeling is applied to get the training data which can save lots of labeling time. Latent Dirichlet Allocation (LDA) model was involved to choose the features for labeling. Experiment results show that the proposed method can dramatically improve the performance of biomedical NER through incorporating unlabeled data by feature labeling.

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“…We use regular expressions to characterize orthographical features which are listed in Table 1. A part of them are also used by Tsai and Settles [33,34].…”

Section: Orthographical Featuresmentioning

confidence: 99%

Biomedical named entity recognition using generalized expectation criteria

Yao

Sun

et al. 2011

Int. J. Mach. Learn. & Cyber.

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“…Conditional random fields (CRF), a type of conditional probability model, has been widely applied in biomedical named entity recognition [7], [28], [34]. The advantage of the CRF model is the ability to express long-distance-dependent and overlapping features.…”

Section: Motivationmentioning

confidence: 99%

Hadoop Recognition of Biomedical Named Entity Using Conditional Random Fields

Tang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Processing large volumes of data has presented a challenging issue, particularly in data-redundant systems. As one of the most recognized models, the conditional random fields (CRF) model has been widely applied in biomedical named entity recognition (Bio-NER). Due to the internally sequential feature, performance improvement of the CRF model is nontrivial, which requires new parallelized solutions. By combining and parallelizing the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and Viterbi algorithms, we propose a parallel CRF algorithm called MRCRF (MapReduce CRF) in this paper, which contains two parallel sub-algorithms to handle two time-consuming steps of the CRF model. The MRLB (MapReduce L-BFGS) algorithm leverages the MapReduce framework to enhance the capability of estimating parameters. Furthermore, the MRVtb (MapReduce Viterbi) algorithm infers the most likely state sequence by extending the Viterbi algorithm with another MapReduce job. Experimental results show that the MRCRF algorithm outperforms other competing methods by exhibiting significant performance improvement in terms of time efficiency as well as preserving a guaranteed level of correctness.Index Terms-Biomedical named entity recognition, conditional random fields, MapReduce, parallel algorithm.

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“…The features they choose include surface word forms, POS tags, orthographic features and head-noun features. Tsai et al (2005) also adopt some linguistic features, orthographical features, context features, POS features, word shape features, prefix and suffix features, and dictionary features to CRF framework. On GENIA 3.02 corpus, their system achieves an Fscore of 78.4% for protein names.…”

Section: Introductionmentioning

confidence: 99%

Conditional Random Fields for Term Extraction

Zhang

Yan

Fang

2010

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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In this paper, we describe how to construct a machine learning framework that utilizes syntactic information in extraction of biomedical terms. Conditional random fields (CRF), is used as the basis of this framework. We make an effort to find the appropriate use for syntactic information, including parent nodes, syntactic paths and term ratios under the machine learning framework. The experiment results show that syntactic paths and term ratios can improve precision of term extraction, including old terms and novel terms. However, the recall rate of novel terms still needs to be increased. This research serves as an example for constructing machine learning based term extraction systems that utilizes linguistic information. 2 RELATED WORKS Different machine learning methods have been used in term identification and term recognition. Zheng et al. (2009) uses six kinds of features in their CRF template, including POS, semantic information, left information entropy, right information entropy, 414 Zhang X., Song Y. and Fang A..

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Integrating linguistic knowledge into a conditional random fieldframework to identify biomedical named entities

Cited by 43 publications

References 42 publications

Biomedical named entity recognition using generalized expectation criteria

Biomedical named entity recognition using generalized expectation criteria

Hadoop Recognition of Biomedical Named Entity Using Conditional Random Fields

Conditional Random Fields for Term Extraction

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