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
The Stanford CoreNLP Natural Language Processing Toolkit
Christopher D. Manning,Mihai Surdeanu,John Bauer,Jenny Rose Finkel,Steven Bethard,David McClosky +5 more
TL;DR: The design and use of the Stanford CoreNLP toolkit is described, an extensible pipeline that provides core natural language analysis, and it is suggested that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
Proceedings ArticleDOI
Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
TL;DR: By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference.
Proceedings ArticleDOI
Nested Named Entity Recognition
TL;DR: This paper presents a new technique for recognizing nested named entities, by using a discriminative constituency parser, which outperforms a standard semi-CRF on the more traditional top-level entities.
Proceedings ArticleDOI
Joint Parsing and Named Entity Recognition
TL;DR: This work proposes a joint model of parsing and named entity recognition, based on a discriminative feature-based constituency parser that produces a consistent output, where the named entity spans do not conflict with the phrasal spans of the parse tree.
Proceedings Article
Efficient, Feature-based, Conditional Random Field Parsing
TL;DR: This work presents the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data, and achieves state-of-the-art results.