Search Results for author: John Pavlopoulos

Found 27 papers, 6 papers with code

ERRANT: Assessing and Improving Grammatical Error Type Classification

no code implementations COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 Katerina Korre, John Pavlopoulos

ERRANT extracts the errors and classifies them into error types, in the form of an edit that can be used in the creation of GEC systems, as well as for grammatical error analysis.

Classification Grammatical Error Correction

From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer

1 code implementation ACL 2022 John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen, Ion Androutsopoulos

We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible.

Toxic Spans Detection

Context Sensitivity Estimation in Toxicity Detection

no code implementations ACL (WOAH) 2021 Alexandros Xenos, John Pavlopoulos, Ion Androutsopoulos

We introduce a new task, context-sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered.

ELERRANT: Automatic Grammatical Error Type Classification for Greek

no code implementations RANLP 2021 Katerina Korre, Marita Chatzipanagiotou, John Pavlopoulos

In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT.

Classification

Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes

no code implementations ACL (WOAH) 2021 Vasiliki Kougia, John Pavlopoulos

The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information.

Classification Multi-Label Classification

Analysing the Greek Parliament Records with Emotion Classification

no code implementations24 May 2022 John Pavlopoulos, Vanessa Lislevand

The potential of the presented resources is investigated by detecting and studying the emotion of `disgust' in the Greek Parliament records.

Classification Emotion Classification

Restoring and attributing ancient texts using deep neural networks

2 code implementations Nature 2022 Yannis Assael, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, Jonathan Prag, Nando de Freitas

Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history.

Ancient Text Restoration

SemEval-2021 Task 5: Toxic Spans Detection

no code implementations SEMEVAL 2021 John Pavlopoulos, Jeffrey Sorensen, L{\'e}o Laugier, Ion Androutsopoulos

For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans.

Toxic Spans Detection

Civil Rephrases Of Toxic Texts With Self-Supervised Transformers

1 code implementation EACL 2021 Leo Laugier, John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon

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

Denoising Self-Supervised Learning +2

Diagnostic Captioning: A Survey

no code implementations18 Jan 2021 John Pavlopoulos, Vasiliki Kougia, Ion Androutsopoulos, Dimitris Papamichail

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

Image Captioning

Clinical Predictive Keyboard using Statistical and Neural Language Modeling

no code implementations22 Jun 2020 John Pavlopoulos, Panagiotis Papapetrou

We show that a neural language model can achieve as high as 51. 3% accuracy in radiology reports (one out of two words predicted correctly).

Language Modelling

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

1 code implementation11 Jun 2020 Vasiliki Kougia, John Pavlopoulos, Panagiotis Papapetrou, Max Gordon

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 exams, and c) providing a diagnostic explanation in natural language for each abnormal exam.

ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT

no code implementations SEMEVAL 2019 John Pavlopoulos, Nithum Thain, Lucas Dixon, Ion Androutsopoulos

This paper presents the application of two strong baseline systems for toxicity detection and evaluates their performance in identifying and categorizing offensive language in social media.

Language Identification

A Survey on Biomedical Image Captioning

2 code implementations WS 2019 Vasiliki Kougia, John Pavlopoulos, Ion Androutsopoulos

Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians.

Image Captioning

Deeper Attention to Abusive User Content Moderation

no code implementations EMNLP 2017 John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos

Experimenting with a new dataset of 1. 6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams.

Word Embeddings

Improved Abusive Comment Moderation with User Embeddings

no code implementations WS 2017 John Pavlopoulos, Prodromos Malakasiotis, Juli Bakagianni, Ion Androutsopoulos

Experimenting with a dataset of approximately 1. 6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases.

Deep Learning for User Comment Moderation

no code implementations WS 2017 John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos

We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.

General Classification

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