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Found 12 papers, 3 papers with code
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews
We provide an overview of the MSLR2022 shared task on multi-document summarization for literature reviews.
Question answering systems for health professionals at the point of care -- a systematic review
Results: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods.
RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media
Using this corpus, we introduce the task of retrieving trustworthy evidence relevant to a given claim made on social media.
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Elastic Weight Consolidation is a recently proposed method to address this issue, but scaling this approach to the modern large models used in practice requires making strong independence assumptions about model parameters, limiting its effectiveness.
Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence
Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search.
Syntactic Patterns Improve Information Extraction for Medical Search
Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest.
Aggregating and Predicting Sequence Labels from Crowd Annotations
Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text.
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification.
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
Graph-Sparse LDA: A Topic Model with Structured Sparsity
In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words.
