Quantitative Assessment of Dictionary-based Protein Named Entity Tagging
نویسندگان
چکیده
منابع مشابه
Quantitative Assessment of Dictionary-based Protein Named Entity Tagging
Objective: Natural language processing (NLP) approaches have been explored to manage and mine information recorded in biological literature. A critical step for biological literature mining is biological named entity tagging (BNET) that identifies names mentioned in text and normalizes them with entries in biological databases. The aim of this study was to provide quantitative assessment of the...
متن کاملResearch Paper: Quantitative Assessment of Dictionary-based Protein Named Entity Tagging
OBJECTIVE Natural language processing (NLP) approaches have been explored to manage and mine information recorded in biological literature. A critical step for biological literature mining is biological named entity tagging (BNET) that identifies names mentioned in text and normalizes them with entries in biological databases. The aim of this study was to provide quantitative assessment of the ...
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We report an empirical study on the role of syntactic features in building a semisupervised named entity (NE) tagger. Our study addresses two questions: What types of syntactic features are suitable for extracting potential NEs to train a classifier in a semi-supervised setting? How good is the resulting NE classifier on testing instances dissimilar from its training data? Our study shows that ...
متن کاملBootstrapping for Named Entity Tagging Using Concept-based Seeds
A novel bootstrapping approach to Named Entity (NE)tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. The bootstrapping procedure is implemented as training two successive learners. First, decision list is used to learn the ...
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3] and [1] opened the possibility of using an unlabeled corpus through co-training, a semi-supervised learning algorithm, to classify named entities. Our approach to solve the problem of Korean named entity classification also adopted a co-training method called DL-CoTrain. However, we use only a part-of-speech tagger and a simple noun phrase chunker instead of a full parser to extract the cont...
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ژورنال
عنوان ژورنال: Journal of the American Medical Informatics Association
سال: 2006
ISSN: 1067-5027,1527-974X
DOI: 10.1197/jamia.m2085