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Works533 from public data
- SemEval-2016 Task 4: Sentiment Analysis in Twitter966
The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams.
- Predicting the Type and Target of Offensive Posts in Social Media938
The Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, is complied and made publicly available.
- SemEval-2017 Task 4: Sentiment Analysis in Twitter931
The fifth year of the Sentiment Analysis in Twitter task is described, with a new language, Arabic, for all subtasks, and information from the profiles of the Twitter users who posted the target tweets made available.
- SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominals926
A new task is introduced, which will be part of SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of common nominals.
- SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)893
The results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval), based on a new dataset, contain over 14,000 English tweets, are presented.
- SemEval-2014 Task 9: Sentiment Analysis in Twitter617
The Sentiment Analysis in Twitter task is described, a continuation of the last year’s task that ran successfully as part of SemEval2013 and introduced three new test sets: regular tweets, sarcastic tweets, and LiveJournal sentences.
- Overview of BioCreative II gene mention recognition560
It is demonstrated that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.
- SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)544
The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish.
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- SemEval-2015 Task 10: Sentiment Analysis in Twitter427
The 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years.
- Fine-Grained Analysis of Propaganda in News Article423
A novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type is proposed, and a novel multi-granularity neural network is designed that outperforms several strong BERT-based baselines.
- A Survey of Confidence Estimation and Calibration in Large Language Models366
The challenges are outlined and recent technical advancements for LLM confidence estimation and calibration are summarized, and their applications are discussed and suggested promising directions for future work are suggested.
- FANG345
This work proposes Factual News Graph, a novel graphical social context representation and learning framework for fake news detection that is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph.
- Automated Fact-Checking for Assisting Human Fact-Checkers334
Here, a survey of the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor includes identifying claims worth fact- checking, detecting relevant previously fact-checked claims, retrieving relevant evidence to fact-check a claim, and actually verifying a claim.
- Factuality challenges in the era of large language models and opportunities for fact-checking319
By analysing the factuality constraints within LLMs and their impact on fact-checking, this work aims to contribute to a path towards maintaining accuracy at a time of confluence of generative artificial intelligence and misinformation.
- SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles300
The results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles are presented and the system submissions and the methods they used are discussed.
- Predicting Factuality of Reporting and Bias of News Media Sources283
This work is interested in characterizing entire news media, an under-studied, but arguably important research problem, both in its own right and as a prior for fact-checking systems.
- SemEval-2016 Task 3: Community Question Answering241
This paper describes the SemEval–2016 Task 3 on Community Question Answering, which was offered in English and Arabic, and found the best systems achieved an official score of 79.19, 76.70, 55.41, and 45.83 in subtasks A, B, C, and D.
- SemEval-2017 Task 3: Community Question Answering233
SemEval–2017 Task 3 on Community Question Answering reran the four subtasks from SemEval-2016, providing all the data from 2015 and 2016 for training, and fresh data for testing, and added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums.
- MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets231
This work proposes MOMENTA (MultimOdal framework for detecting harmful memes, a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes and outperforms several strong rivaling approaches.
- Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs228
The first open-source dataset to evaluate safeguards in LLMs is collected, and several BERT-like classifiers are trained that can achieve results that are comparable with GPT-4 on automatic safety evaluation.
- SemEval-2007 task 04: classification of semantic relations between nominals222
An evaluation task designed to provide a framework for comparing different approaches to classifying semantic relations between nominals in a sentence as part of SemEval, the 4th edition of the semantic evaluation event previously known as SensEval.
- A Survey on Computational Propaganda Detection219
The state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis is reviewed, arguing about the need for combined efforts between these communities.
- On the effect of dropping layers of pre-trained transformer models207
The lower layers are most critical to maintain downstream task performance, and some tasks such as paraphrase de-tection and sentence similarity are more robust to the dropping of layers, and models trained using different objective function exhibit di-erent learning patterns and w.r.t the layer dropping.
- We Can Detect Your Bias: Predicting the Political Ideology of News Articles207
A large dataset of articles that were manually annotated for political ideology -left, center, or right-, which is well-balanced across both topics and media, and an adversarial media adaptation, as well as a specially adapted triplet loss are proposed.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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