Jay DeYoung
Publications13
Citations33
Highly Influential Citations2
- Publications
- Influence
Entity linking requires ranking thousands of candidates for each query, a time consuming process and a challenge for large scale linking. Many systems rely on prediction cascades to efficiently rank… Continue Reading
We describe a multifaceted approach to named entity recognition that can be deployed with minimal data resources and a handful of hours of non-expert annotation. We describe how this approach was… Continue Reading
We introduce PARMA, a system for crossdocument, semantic predicate and argument alignment. Our system combines a number of linguistic resources familiar to researchers in areas such as recognizing… Continue Reading
Work on cross document coreference resolution (CDCR) has primarily focused on news articles, with little to no work for social media. Yet social media may be particularly challenging since short… Continue Reading
How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated… Continue Reading
Natural language processing research increasingly relies on the output of a variety of syntactic and semantic analytics. Yet integrating output from multiple analytics into a single framework can be… Continue Reading
We explore a human-driven approach to annotation, curated training (CT), in which annotation is framed as teaching the system by using interactive search to identify informative snippets of text to… Continue Reading
We participated in the Event Argument Extraction and Linking (EAEL) task in TAC KBP 2017. We trained separate event anchor, event argument, and realis models using logistic regression on the ACE,… Continue Reading
State-of-the-art models in NLP are now predominantly based on deep neural networks that are generally opaque in terms of how they come to specific predictions. This limitation has led to increased… Continue Reading