S. V. Ramanan

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The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of(More)
We refined the performance of Co-coa/Peaberry, a linguistically motivated system, on extracting disease entities from clinical notes in the training and development sets for Task 7. Entities were identified in noun chunks by use of dictionaries, and events ('The left atrium is dilated') through our own parser and predicate-argument structures. We also(More)
We extended Cocoa/Peaberry, our (RelAgent) existing rule based entity and event tagger, to tag attributes associated with diseases in clinical records. The boolean attributes of Negation, Uncertainty and Conditional were handled by an extension of the NegEx algorithm. The multi-valued Course and Severity attributes were detected either within the extended(More)
We tested the performance of Cocoa, an existing dictio-nary/rule based entity tagger that tags multiple semantic types in biomed-ical domain including diseases, on disease/sign/symptom detection in clinical records in the ShARe/CLEF eHealth task. Initial analysis showed that the precision was high (≥ 90%), but recall was low (≈ 50%) due to (a) phrases(More)