about
Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer DrugsA context-aware approach for progression tracking of medical concepts in electronic medical recordsFeature engineering for MEDLINE citation categorization with MeSHBiomedical question answering using semantic relationsA pipeline to extract drug-adverse event pairs from multiple data sourcesClustering cliques for graph-based summarization of the biomedical research literatureImproving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation.A UMLS-based spell checker for natural language processing in vaccine safetyConcept annotation in the CRAFT corpus.Using semantic predications to uncover drug-drug interactions in clinical data.Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.Extraction of data deposition statements from the literature: a method for automatically tracking research results.Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of diseaseMedical facts to support inferencing in natural language processing.dTagger: a POS taggerBenchmarking natural-language parsers for biological applications using dependency graphs.Knowledge-based methods to help clinicians find answers in MEDLINE.Comparative analysis of five protein-protein interaction corporaSemantic role labeling for protein transport predicates.Identifying risk factors for metabolic syndrome in biomedical text.Abbreviation definition identification based on automatic precision estimates.What can natural language processing do for clinical decision support?Biomedical text mining and its applications.Click-words: learning to predict document keywords from a user perspectiveCombining relevance assignment with quality of the evidence to support guideline development.Natural language processing pipelines to annotate BioC collections with an application to the NCBI disease corpus.A Study of the Morpho-Semantic Relationship in MedlineBioC interoperability track overviewAssigning factuality values to semantic relations extracted from biomedical research literature.Building a biomedical tokenizer using the token lattice design pattern and the adapted Viterbi algorithm.A context-blocks model for identifying clinical relationships in patient records.Machine learning with naturally labeled data for identifying abbreviation definitionsConstructing a semantic predication gold standard from the biomedical literature.Finding translational science publications in MEDLINE/PubMed with translational science filtersIdentifying well-formed biomedical phrases in MEDLINE® text.Finding related sentence pairs in MEDLINE.Finding biomedical categories in Medline®.Chapter 16: text mining for translational bioinformatics.A methodology for extending domain coverage in SemRep.BioC: a minimalist approach to interoperability for biomedical text processing
P2860
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P2860
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
MedPost: a part-of-speech tagger for bioMedical text.
@en
type
label
MedPost: a part-of-speech tagger for bioMedical text.
@en
prefLabel
MedPost: a part-of-speech tagger for bioMedical text.
@en
P356
P1433
P1476
MedPost: a part-of-speech tagger for bioMedical text.
@en
P2093
P304
P356
10.1093/BIOINFORMATICS/BTH227
P407
P577
2004-04-08T00:00:00Z