activity
20172022
most citedA Survey on Biomedical Image Captioning

13 citations · 14 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL20221 cited

A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

Konstantina Dritsa, Kaiti Thoma, John Pavlopoulos +1

Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as Greek. In this paper, we introduce a curated dataset of th…

cs.CL2021

Civil Rephrases Of Toxic Texts With Self-Supervised Transformers

Leo Laugier, John Pavlopoulos, Jeffrey Sorensen +1

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does…

cs.CV2021

Diagnostic Captioning: A Survey

John Pavlopoulos, Vasiliki Kougia, Ion Androutsopoulos +1

Diagnostic Captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination. DC can assist inexperie…

cs.CL2020

Clinical Predictive Keyboard using Statistical and Neural Language Modeling

John Pavlopoulos, Panagiotis Papapetrou

A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails). Language models, however, have only b…

cs.CV2020

RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams

Vasiliki Kougia, John Pavlopoulos, Panagiotis Papapetrou +1

This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal e…

cs.CL2020

Toxicity Detection: Does Context Really Matter?

John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon +2

Moderation is crucial to promoting healthy on-line discussions. Although several `toxicity' detection datasets and models have been published, most of them ignore the context of th…