2014
DOI: 10.1186/1471-2105-15-160
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A resource-saving collective approach to biomedical semantic role labeling

Abstract: BackgroundBiomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been su… Show more

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Cited by 5 publications
(5 citation statements)
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“…BioSRL is usually formulated as a supervised machine learning problem that relies on manually annotated training corpora ( 4 , 13 ). However, building such large corpora requires much human effort.…”
Section: Discussionmentioning
confidence: 99%
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“…BioSRL is usually formulated as a supervised machine learning problem that relies on manually annotated training corpora ( 4 , 13 ). However, building such large corpora requires much human effort.…”
Section: Discussionmentioning
confidence: 99%
“…Third, function patterns are used to determine the functions of the NEs. Finally, the SRL-based method classifies ( 4 ) the causal and correlative relationships.…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…In the SRL stage, two systems, RCBiosmile (22) and Enju (23), are employed. Enju covers some predicates (verbs) not recognized by RCBiosmile, which is trained on the BioProp corpus (24).…”
Section: Methodsmentioning
confidence: 99%