Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
نویسندگان
چکیده
منابع مشابه
Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER...
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Recently, various machine learning models have been built using word-level embeddings and have achieved substantial improvement in NER prediction accuracy. Most NER models only take words as input and ignore character-level information. In this paper, we propose an effective word representation that efficiently includes both the word-level and character-level information by averaging its charac...
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We demonstrate that bootstrapping a gene name recognizer for FlyBase curation from automatically annotated noisy text is more effective than fully supervised training of the recognizer on more general manually annotated biomedical text. We present a new test set for this task based on an annotation scheme which distinguishes gene names from gene mentions, enabling a more consistent annotation. ...
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ژورنال
عنوان ژورنال: BioMed Research International
سال: 2014
ISSN: 2314-6133,2314-6141
DOI: 10.1155/2014/240403