most citedRefining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

3 citations · 6 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CL2022

Nearest Neighbor Language Models for Stylistic Controllable Generation

Severino Trotta, Lucie Flek, Charles Welch

Recent language modeling performance has been greatly improved by the use of external memory. This memory encodes the context so that similar contexts can be recalled during decodi…

cs.CL20221 cited

How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application

Ramit Sawhney, Atula Tejaswi Neerkaje, Ivan Habernal +1

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance…

cs.CL20222 cited

Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy

Allison Lahnala, Charles Welch, Béla Neuendorf +1

Large pre-trained neural language models have supported the effectiveness of many NLP tasks, yet are still prone to generating toxic language hindering the safety of their use. Usi…

cs.SI2022

FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias

Flora Sakketou, Joan Plepi, Riccardo Cervero +3

Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Redd…

cs.CL2022

Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion

Flora Sakketou, Allison Lahnala, Liane Vogel +1

There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However,…

cs.AI20223 cited

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Niclas Heilig, Jan Kirchhoff, Florian Stumpe +3

Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowled…