Recognizing names in biomedical texts: a machine learning approach

Zhou, Guodong; Zhang, Jie; Su, Jian; Shen, Dan; Tan, Chew-Lim

doi:10.1093/bioinformatics/bth060

Cited by 196 publications

(108 citation statements)

References 21 publications

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“…The process of reading and organizing media clips in a structured manner when a large corpus of newspaper clippings is available requires the implementation of Information Extraction systems, which allows for structuring free text in order to gather information about pre-specified events [10,11] for subsequent statistical analyses.…”

Section: Discussionmentioning

confidence: 99%

“…In this sense, recognizing clinical words becomes a central issue [10,11]: solutions to it are basically (a) the unfeasible by-hand revision of entire sets of newspapers and (b) the implementation of automated procedures aimed at extracting information. In more technical terms, the second approach means that the issue has to be faced by implementing Information Extraction (IE) techniques.…”

Section: Introductionmentioning

confidence: 99%

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Information Extraction Approaches to Unconventional Data Sources for “Injury Surveillance System”: the Case of Newspapers Clippings

Berchialla¹,

Scarinzi

Snidero

et al. 2010

J Med Syst

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Injury Surveillance Systems based on traditional hospital records or clinical data have the advantage of being a well established, highly reliable source of information for making an active surveillance on specific injuries, like choking in children. However, they suffer the drawback of delays in making data available to the analysis, due to inefficiencies in data collection procedures. In this sense, the integration of clinical based registries with unconven-tional data sources like newspaper articles has the advan-tage of making the system more useful for early alerting. Usage of such sources is difficult since information is only available in the form of free natural-language documents rather than structured databases as required by traditional data mining techniques. Information Extraction (IE) addresses the problem of transforming a corpus of textual documents into a more structured database. In this paper, on a corpora of Italian newspapers articles related to choking in children due to ingestion/inhalation of foreign body we compared the performance of three IE algorithms-(a) a classical rule based system which requires a manual annotation of the rules; (ii) a rule based system which allows for the automatic building of rules; (b) a machine learning method based on Support Vector Machine. Although some useful indications are extracted from the newspaper clippings, this approach is at the time far from being routinely implemented for injury surveillance pur-poses.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Information Extraction Approaches to Unconventional Data Sources for “Injury Surveillance System”: the Case of Newspapers Clippings

Berchialla¹,

Scarinzi

Snidero

et al. 2010

J Med Syst

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“…Some of our fterm dictionary is shown in Table 3: Morphological features as suffix and prefix are considered as important terminology cue for classification and have been widely used in biomedical domain. Similar to Zhou et al [5], we use statistical method to get the most frequent suffixes and prefixes from training data as candidates. Then, each of those candidates is sorted using formula below:…”

Section: Construction Of Dictionariesmentioning

confidence: 99%

“…Sentence: IL-2 gene expression and NF-kappaB activation through CD28 requires reactive oxygen production by 5 …”

Section: Curating Dictionarymentioning

confidence: 99%

“…Nowadays, Named Entity Recognition (NER) is proved to be fundamental in information extraction and understanding in biomedical domain. Based on the method, the NER system can be roughly split into three categorizes: rule-based methods [1][2], dictionary-based methods [3], and statistical-based methods [4][5][6][7], although there are also combination of dictionary-based and rule-based method [8].…”

Section: Introductionmentioning

confidence: 99%

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Various Features with Integrated Strategies for Protein Name Classification

Ongkowijaya

Ding

Zhu

2005

Parallel and Distributed Processing and Applications - ISPA 2005 Workshops

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Abstract. Classification task is an integral part of named entity recognition system to classify a recognized named entity to its corresponding class. This task has not received much attention in the biomedical domain, due to the lack of awareness to differentiate feature sources and strategies in previous studies.In this research, we analyze different sources and strategies of protein name classification, and developed integrated strategies that incorporate advantages from rule-based, dictionary-based and statistical-based method. In rule-based method, terms and knowledge of protein nomenclature that provide strong cue for protein name are used. In dictionary-based method, a set of rules for curating protein name dictionary are used. These terms and dictionaries are combined with our developed features into a statistical-based classifier. Our developed features are comprised of word shape features and unigram & bi-gram features. Our various information sources and integrated strategies are able to achieve state-of-the-art performance to classify protein and non-protein names.

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