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Found 18 papers, 3 papers with code
On-the-fly Definition Augmentation of LLMs for Biomedical NER
In this work we set out to improve LLM performance on biomedical NER in limited data settings via a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly.
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Our method entails tasking an LLM to infer whether a patient has, or is at risk of, a particular condition on the basis of associated notes; if so, we ask the model to summarize the supporting evidence.
Revisiting Relation Extraction in the era of Large Language Models
Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text.
Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs
However, results from RCTs are presented in (often unstructured) natural language articles describing the design, execution, and outcomes of trials; clinicians must manually extract findings pertaining to interventions and outcomes of interest from such articles.
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.
On the Impact of Random Seeds on the Fairness of Clinical Classifiers
Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s).
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.
Mental Health Surveillance over Social Media with Digital Cohorts
The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance.
Quantifying Mental Health from Social Media with Neural User Embeddings
Our experimental results demonstrate that the user embeddings capture similarities between users with respect to mental conditions, and are predictive of mental health.
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
This allows us to expand lexicons describing multiple semantic properties.
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
We introduce a deep neural network for automated sarcasm detection.
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs.
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