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Found 41 papers, 14 papers with code
Context Sensitivity Estimation in Toxicity Detection
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.
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer
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.
ERRANT: Assessing and Improving Grammatical Error Type Classification
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.
Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes
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.
Enriching Grammatical Error Correction Resources for Modern Greek
In addition, we provide an extended version of the Greek Learner Corpus (GLC), on which our model reaches up to 22. 76% F0. 5.
A Study of Distant Viewing of ukiyo-e prints
In this paper, we explored a new analytical approach based on the macroanalysis of images facilitated by Natural Language Processing technologies.
Sentiment Analysis of Homeric Text: The 1st Book of Iliad
By estimating the fraction of annotators that found a verse as belonging to a specific sentiment class, we model the poem’s perceived sentiment as a multi-variate time series.
ELERRANT: Automatic Grammatical Error Type Classification for Greek
In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT.
Handwritten Paleographic Greek Text Recognition: A Century-Based Approach
The overall aim of this paper is to assess HTR for old Greek manuscripts.
Leveraging LLMs for Translating and Classifying Mental Health Data
In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and reduce long waiting times for patients.
Severity Prediction in Mental Health: LLM-based Creation, Analysis, Evaluation of a Novel Multilingual Dataset
Large Language Models (LLMs) are increasingly integrated into various medical fields, including mental health support systems.
Evaluating the Reliability of Self-Explanations in Large Language Models
This paper investigates the reliability of explanations generated by large language models (LLMs) when prompted to explain their previous output.
Towards Systematic Monolingual NLP Surveys: GenA of Greek NLP
By applying our method, we conducted a systematic literature review of Greek NLP from 2012 to 2022, providing a comprehensive overview of the current state and challenges of Greek NLP research.
A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning
The accuracy of a diagnostic text, however, strongly depends on how well the key medical conditions depicted in the images are expressed.
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Contaminated or adulterated food poses a substantial risk to human health.
Revisiting Silhouette Aggregation
Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment.
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as Greek.
Automotive Multilingual Fault Diagnosis
Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics.
Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts
This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models.
Analysing the Greek Parliament Records with Emotion Classification
The potential of the presented resources is investigated by detecting and studying the emotion of `disgust' in the Greek Parliament records.
Restoring and attributing ancient texts using deep neural networks
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.
Ranked #1 on
Ancient Text Restoration
on I.PHI
Toxicity Detection can be Sensitive to the Conversational Context
User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets.
SemEval-2021 Task 5: Toxic Spans Detection
For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans.
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers
Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts.
Diagnostic Captioning: A Survey
Diagnostic Captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination.
Clinical Predictive Keyboard using Statistical and Neural Language Modeling
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).
RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams
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.
Toxicity Detection: Does Context Really Matter?
Moderation is crucial to promoting healthy on-line discussions.
ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT
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.
A Survey on Biomedical Image Captioning
Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians.
Deeper Attention to Abusive User Content Moderation
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.
Improved Abusive Comment Moderation with User Embeddings
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
We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.
