The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.
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Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational researchOpenDMAP: an open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expressionExploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer DrugsThe Implicitome: A Resource for Rationalizing Gene-Disease AssociationsLearning the Structure of Biomedical Relationships from Unstructured TextReasoning with Vectors: A Continuous Model for Fast Robust InferenceKnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciencesLearning to identify treatment relations in clinical textIdentifying plausible adverse drug reactions using knowledge extracted from the literatureLabeledIn: cataloging labeled indications for human drugsBiomedical question answering using semantic relationsSurveillance for the prevention of chronic diseases through information associationPREDOSE: a semantic web platform for drug abuse epidemiology using social mediaClustering cliques for graph-based summarization of the biomedical research literatureText summarization as a decision support aidEnhancing biomedical text summarization using semantic relation extractionBiomedical text summarization to support genetic database curation: using Semantic MEDLINE to create a secondary database of genetic informationUsing contextual and lexical features to restructure and validate the classification of biomedical conceptsIntegration of data from omic studies with the literature-based discovery towards identification of novel treatments for neovascularization in diabetic retinopathy.Using semantic predications to uncover drug-drug interactions in clinical data.Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.Semantic processing to support clinical guideline development.Interpreting hypernymic propositions in an online medical encyclopediaExploring semantic groups through visual approaches.Differentiating Sense through Semantic Interaction Data.Literature-Based Discovery of Confounding in Observational Clinical DataIntegrating a hypernymic proposition interpreter into a semantic processor for biomedical texts.Medical facts to support inferencing in natural language processing.Semantic processing to enhance retrieval of diagnosis citations from MedlineSemantic classification of biomedical concepts using distributional similarity.Knowledge-based methods to help clinicians find answers in MEDLINE.Using the literature-based discovery paradigm to investigate drug mechanismsIdentifying risk factors for metabolic syndrome in biomedical text.Leveraging semantic knowledge in IRB databases to improve translation science.Fine-grained indexing of the biomedical literature: MeSH subheading attachment for a MEDLINE indexing tool.Automatic summarization of MEDLINE citations for evidence-based medical treatment: a topic-oriented evaluation.Mining clinical relationships from patient narrativesA recent advance in the automatic indexing of the biomedical literature.Characterizing environmental and phenotypic associations using information theory and electronic health records.The potential for automated question answering in the context of genomic medicine: an assessment of existing resources and properties of answers.
P2860
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P2860
The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.
description
2003 nî lūn-bûn
@nan
2003 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
The interaction of domain know ...... opositions in biomedical text.
@ast
The interaction of domain know ...... opositions in biomedical text.
@en
type
label
The interaction of domain know ...... opositions in biomedical text.
@ast
The interaction of domain know ...... opositions in biomedical text.
@en
prefLabel
The interaction of domain know ...... opositions in biomedical text.
@ast
The interaction of domain know ...... opositions in biomedical text.
@en
P1476
The interaction of domain know ...... opositions in biomedical text.
@en
P2093
Marcelo Fiszman
Thomas C Rindflesch
P304
P356
10.1016/J.JBI.2003.11.003
P577
2003-12-01T00:00:00Z