The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text

By Thomas C. Rindflesch, Marcelo Fiszman. Journal of Biomedical Informatics, 2003

566 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/30bafa2d2f9dbe9e89c0efae1e4571809d383328

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Citations in the corpus

A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations (2018, methodology): “2 SVM with SemMed Database In the last set of experiments, we study the performances of SODDA with the (b, c, d) selected in the previous section and RADiSA-avg on the Semantic MEDLINE Database [9] with SemRep, a semantic interpreter of biomedical text [16] as an extraction tool to construct the kno”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, unknown): “Some clinical NLP systems apply hand-crafted syntactic and semantic rules to extract prespecified semantic relations, such as MedLEE [106] and SemRep [107], but are hard to adapt to new subdomains.”
Context-Driven Automatic Subgraph Creation for Literature-Based Discovery (2015, methodology): “These predications are extracted directly from assertions in scientific literature, using SemRep [44].”
Frontiers of biomedical text mining: current progress (2007, methodology): “[101] help provide more precise information about the ‘B’ terms, leveraging the ‘semantic predications’ extracted by BioMedLEE [45] and SemRep [109] for B- or C-related literature.”
Semi-Supervised Learning to Identify UMLS Semantic Relations (2014, methodology): “Clinical NLP systems such as MedLEE [9] and SemRep [10] apply hand-crafted syntactic and semantic rules to extract UMLS semantic relations.”
Automatic lymphoma classification with sentence subgraph mining from pathology reports. (2014, methodology): “Other systems apply hand-crafted rules to extract pre-specified semantic relations, such as MedLEE [28], MedEx [29] and SemRep [30], or require supervised learning on pre-specified semantic relations, such as [31], thus are hard to adapt to new subdomains.”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, methodology): “Some clinical NLP systems apply hand-crafted syntactic and semantic rules to extract pre-specified semantic relations, such as MedLEE [56] and SemRep [57], but are hard to adapt to new subdo-mains.”
Extracting Semantic Predications from Medline Citations for Pharmacogenomics (2006, methodology): “Enhanced SemRep is based on two existing systems: SemRep [8,9] and SemGen [10,11].”