J
Jenny Rose Finkel
Researcher at Stanford University
Publications - 17
Citations - 12044
Jenny Rose Finkel is an academic researcher from Stanford University. The author has contributed to research in topics: Named-entity recognition & Biomedical text mining. The author has an hindex of 15, co-authored 17 publications receiving 10862 citations.
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Proceedings ArticleDOI
Hierarchical Bayesian Domain Adaptation
TL;DR: It is demonstrated that allowing different values for these hyperparameters significantly improves performance over both a strong baseline and (Daume III, 2007) within both a conditional random field sequence model for named entity recognition and a discriminatively trained dependency parser.
Proceedings ArticleDOI
Exploiting context for biomedical entity recognition: from syntax to the web
Jenny Rose Finkel,Shipra Dingare,Huy Nguyen,Malvina Nissim,Christopher D. Manning,Gail Sinclair +5 more
TL;DR: A machine learning system for the recognition of names in biomedical texts that makes extensive use of local and syntactic features within the text, as well as external resources including the web and gazetteers.
Proceedings ArticleDOI
Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines
TL;DR: A novel architecture is presented, which models these pipelines as Bayesian networks, with each low level task corresponding to a variable in the network, and then it is performed approximate inference to find the best labeling.
Journal ArticleDOI
Exploring the boundaries: gene and protein identification in biomedical text.
Jenny Rose Finkel,Shipra Dingare,Christopher D. Manning,Malvina Nissim,Beatrice Alex,Claire Grover +5 more
TL;DR: Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.
Proceedings ArticleDOI
Enforcing Transitivity in Coreference Resolution
TL;DR: This work trains a coreference classifier over pairs of mentions, and shows how to encode this type of constraint on top of the probabilities output from the pairwise classifier to extract the most probable legal entity assignments.