S
Steven Bethard
Researcher at University of Arizona
Publications - 165
Citations - 13414
Steven Bethard is an academic researcher from University of Arizona. The author has contributed to research in topics: Computer science & Task (project management). The author has an hindex of 39, co-authored 143 publications receiving 11495 citations. Previous affiliations of Steven Bethard include University of Alabama at Birmingham & Stanford University.
Papers
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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 Article
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
Vikas Yadav,Steven Bethard +1 more
TL;DR: This work presents a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms.
Proceedings Article
Automatic Extraction of Opinion Propositions and their Holders
TL;DR: An extension of semantic parsing techniques, coupled with additional lexical and syntactic features, that can produce labels for propositional opinions as opposed to other syntactic constituents is proposed.
Proceedings ArticleDOI
How Good Are Humans at Solving CAPTCHAs? A Large Scale Evaluation
TL;DR: In this paper, a large scale evaluation of captchas from the human perspective is presented, with the goal of assessing how much friction CAPTCHAs present to the average user.
How good are humans at solving captchas a large scale evaluation
TL;DR: Evidence from a week’s worth of eBay captchas suggests that the solving accuracies found in the study are close to real-world values, and that improving audioCaptchas should become a priority, as nearly 1% of all captchAs are delivered as audio rather than images.