Jay DeYoung
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View article: Novel Breast Reconstruction Generative Pretrained Transformer Promotes Decisional Confidence and Clinical Efficiency
Novel Breast Reconstruction Generative Pretrained Transformer Promotes Decisional Confidence and Clinical Efficiency Open
Summary: Patients undergoing breast reconstruction face many decisions. A generative pretrained transformer (GPT) tool was custom built to assist patients at their initial reconstructive consultation. Twenty patients undergoing immediate b…
View article: <i>Literature search sandbox</i>: a large language model that generates search queries for systematic reviews
<i>Literature search sandbox</i>: a large language model that generates search queries for systematic reviews Open
Objectives Development of search queries for systematic reviews (SRs) is time-consuming. In this work, we capitalize on recent advances in large language models (LLMs) and a relatively large dataset of natural language descriptions of revi…
View article: Do Multi-Document Summarization Models <i>Synthesize</i>?
Do Multi-Document Summarization Models <i>Synthesize</i>? Open
Multi-document summarization entails producing concise synopses of collections of inputs. For some applications, the synopsis should accurately synthesize inputs with respect to a key aspect, e.g., a synopsis of film reviews written about …
View article: Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations Open
Evaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contradicting evidence reported across different documents. Prio…
View article: Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs
Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs Open
Results from Randomized Controlled Trials (RCTs) establish the comparative effectiveness of interventions, and are in turn critical inputs for evidence-based care. However, results from RCTs are presented in (often unstructured) natural la…
View article: Do Multi-Document Summarization Models Synthesize?
Do Multi-Document Summarization Models Synthesize? Open
Multi-document summarization entails producing concise synopses of collections of inputs. For some applications, the synopsis should accurately synthesize inputs with respect to a key aspect, e.g., a synopsis of film reviews written about …
View article: Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations Open
Evaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contradicting evidence reported across different documents. Prio…
View article: Entity Anchored ICD Coding
Entity Anchored ICD Coding Open
Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the …
View article: MS2: Multi-Document Summarization of Medical Studies
MS2: Multi-Document Summarization of Medical Studies Open
To assess the effectiveness of any medical intervention, researchers must conduct a time-intensive and highly manual literature review. NLP systems can help to automate or assist in parts of this expensive process. In support of this goal,…
View article: MSˆ2: Multi-Document Summarization of Medical Studies
MSˆ2: Multi-Document Summarization of Medical Studies Open
To assess the effectiveness of any medical intervention, researchers must conduct a time-intensive and manual literature review. NLP systems can help to automate or assist in parts of this expensive process. In support of this goal, we rel…
View article: Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations Open
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-mak…
View article: Evidence Inference 2.0: More Data, Better Models
Evidence Inference 2.0: More Data, Better Models Open
How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead dis…
View article: ERASER: A Benchmark to Evaluate Rationalized NLP Models
ERASER: A Benchmark to Evaluate Rationalized NLP Models Open
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased interest in designing more interpretable deep models for NLP th…
View article: Neural-Network Lexical Translation for Cross-lingual IR from Text and Speech
Neural-Network Lexical Translation for Cross-lingual IR from Text and Speech Open
We propose a neural network model to estimate word translation probabilities for Cross-Lingual Information Retrieval (CLIR). The model estimates better probabilities for word translations than automatic word alignments alone, and generaliz…
View article: Inferring Which Medical Treatments Work from Reports of Clinical Trials
Inferring Which Medical Treatments Work from Reports of Clinical Trials Open
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 in natural language scientific articles…
View article: Events Beyond ACE: Curated Training for Events
Events Beyond ACE: Curated Training for Events Open
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 annotate, unlike traditional approaches w…
View article: Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation
Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation Open
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 messages provide little context, and informal…
View article: A Concrete Chinese NLP Pipeline
A Concrete Chinese NLP Pipeline Open
Nanyun Peng, Francis Ferraro, Mo Yu, Nicholas Andrews, Jay DeYoung, Max Thomas, Matthew R. Gormley, Travis Wolfe, Craig Harman, Benjamin Van Durme, Mark Dredze. Proceedings of the 2015 Conference of the North American Chapter of the Associ…