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== Citation == Daniel Gildea and Daniel Jurafsky. 2002. Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3):245-288. == Online version == [http://www.mitpressjournals.org/doi/pdf/10.1162/089120102760275983 MIT Press] == Summary == The [[Category::paper]] presents a system for [[AddressesProblem:Semantic Role Labeling|semantic role labeling]]. They divided their extraction job into three tasks below. * Extraction of medical terms * [[AddressesProblem::Relation Extraction]] **In this paper, relation extraction means extraction of associated medical concepts. For example, 'Blood pressure' and '144/90' are associated terms in the sentence, "Blood pressure is 144/90". * [[AddressesProblem::Text Classification]] **For example, a patient can be classified as a former smoker, a current smoker, or a non-smoker Their approaches are: * An ontology-based approach for extracting medical terms of interest **They used Unified Medical Language System (UMLS). About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures. * A graph-based approach which uses the parsing result of link-grammar parser for [[AddressesProblem::Relation Extraction]] **Notable things in their approach are three. First, they included the processing of negation. Second, when the parser fails, they used a pattern-based approach. Lastly, they replaced multi-word terms with placeholders because the parser did not process the terms. * an NLP-based feature extraction method coupled with an ID3-based [[AddressesProblem::Decision Tree Learning]] for [[AddressesProblem::Text Classification]] This approach was fairly successful mostly showing over 80% of precision and recall. However, the system was tested on the data written by only a clinician, which means that the style of free-text records was consistent. Nevertheless, the research is worth in that they applied various IE techniques to the free-text clinical records, explain about the problems they encountered. == Related papers == An interesting follow-up paper is [[RelatedPaper::Denecke and Bernauer AIME 2007]] which uses semantic structures to extract medical information.

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