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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora Groza, Tudor; Köhler, Sebastian; Doelken, Sandra. - : Oxford University Press, 2015 BASE Show details 2 A Supervised Approach to Quantifying Sentence Similarity: With Application to Evidence Based Medicine Hassanzadeh, Hamed; Groza, Tudor; Nguyen, Anthony. - : Public Library of Science, 2015 BASE Show details 3 Similarity metrics for clustering PubMed abstracts for evidence based medicine Hassanzadeh, Hamed; Mollá, Diego; Groza, Tudor. - : Melbourne, Australia : Association for Computational Linguistics, 2015 BASE Show details 4 Generation of Silver Standard Concept Annotations from Biomedical Texts with Special Relevance to Phenotypes Oellrich, Anika; Collier, Nigel; Smedley, Damian. - : Public Library of Science, 2015 BASE Show details 5 A Supervised Approach to Quantifying Sentence Similarity: With Application to Evidence Based Medicine Hassanzadeh, Hamed; Groza, Tudor; Nguyen, Anthony. - : Public Library of Science, 2015 BASE Show details 6 Generation of silver standard concept annotations from Biomedical texts with special relevance to phenotypes Oellrich, Anika; Collier, Nigel; Smedley, Damian. - : Public Library of Science, 2015 BASE Show details 7 Extracting structured data from publications in the Art Conservation Domain Odat, Suleiman; Groza, Tudor; Hunter, Jane. - : Oxford University Press, 2014 Abstract: The most common method of publishing new discoveries about art conservation techniques and research has been through traditional full-text publications. Such corpora typically only support searching via metadata (e.g. title, authors, or keywords) and full-text. In particular, it is difficult to discover valuable information about the chemical processes, experimental results, or preservation treatments associated with the conservation of paintings from a specific genre. This article addresses this problem by focusing on the extraction of structured data (that complies with a pre-defined ontology) from a distributed corpus of publications about painting conservation. Our specific extraction method involves a unique combination of named entity recognition (using gazetteer-based and machine learning-based methods) followed by relationship extraction (using rule-based and machine learning-based methods). The resulting structured data are stored in a resource description framework triple store, and a Web-based graphical user interface enables the SPARQL querying, retrieval, and display of the search results. The results from applying our techniques to a corpus of publications on art conservation indicate that our approach achieves higher quality precision and recall in extracting named entities and relations from publications, relative to alternative existing approaches. Keyword: 1203 Language and Linguistics; 1706 Computer Science Applications; 1710 Information Systems; 3310 Linguistics and Language; Computer Science Applications; Information Systems; Language and Linguistics; Linguistics and Language URL: https://espace.library.uq.edu.au/view/UQ:327177 BASE Hide details 8 Using silver and semi-gold standard corpora to compare open named entity recognisers Groza, Tudor; Oellrich, Anika; Collier, Nigel. - : I E E E, 2013 BASE Show details 9 Using typed dependencies to study and recognise conceptualisation zones in biomedical literature Groza, Tudor. - : Public Library of Science, 2013 BASE Show details

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