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Found 40 papers, 20 papers with code
Comparative Analysis of Fine-tuned Deep Learning Language Models for ICD-10 Classification Task for Bulgarian Language
In our research study all BERT models are fine-tuned with additional medical texts in Bulgarian and then applied to the classification task for encoding medical diagnoses in Bulgarian into ICD-10 codes.
Automatic Transformation of Clinical Narratives into Structured Format
For the “Diagnosis” section a deep learning text-based encoding into ICD-10 codes is applied using MBG-ClinicalBERT - a fine-tuned ClinicalBERT model for Bulgarian medical text.
A Comparative Study on Abstractive and Extractive Approaches in Summarization of European Legislation Documents
A simple extractive algorithm was selected as a baseline.
EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image.
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning
Through a comparative study, we demonstrate the superiority of our method over existing FL data partitioning approaches, showcasing its potential to challenge model aggregation algorithms.
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles
Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information.
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
DuoSearch: A Novel Search Engine for Bulgarian Historical Documents
Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes.
Optical Character Recognition
Optical Character Recognition (OCR)
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data
Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers.
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media
Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision.
Leaf: Multiple-Choice Question Generation
Testing with quiz questions has proven to be an effective way to assess and improve the educational process.
SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.
Exposing Paid Opinion Manipulation Trolls
Recently, Web forums have been invaded by opinion manipulation trolls.
Generating Answer Candidates for Quizzes and Answer-Aware Question Generators
In education, open-ended quiz questions have become an important tool for assessing the knowledge of students.
Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels
We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels.
EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains.
Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models
While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic.
Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection
Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset.
A Context-Aware Approach for Detecting Check-Worthy Claims in Political Debates
In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking.
In Search of Credible News
As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources.
Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information
Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only.
Detecting Deception in Political Debates Using Acoustic and Textual Features
We present work on deception detection, where, given a spoken claim, we aim to predict its factuality.
Deep learning contextual models for prediction of sport event outcome from sportsman's interviews
We test the hypothesis that the sports results can be predicted by using natural language processing and machine learning techniques applied over interviews with the players shortly before the sport events.
Fact-Checking Meets Fauxtography: Verifying Claims About Images
The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives.
Detecting Toxicity in News Articles: Application to Bulgarian
Online media aim for reaching ever bigger audience and for attracting ever longer attention span.
Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian
Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.
Recursive Style Breach Detection with Multifaceted Ensemble Learning
We present a supervised approach for style change detection, which aims at predicting whether there are changes in the style in a given text document, as well as at finding the exact positions where such changes occur.
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots
Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents.
Towards Automated Customer Support
Recent years have seen growing interest in conversational agents, such as chatbots, which are a very good fit for automated customer support because the domain in which they need to operate is narrow.
Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine
We present the system built for SemEval-2018 Task 2 on Emoji Prediction.
We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!
So, we did this research on fake news/click-bait detection and trust us, it is totally great research, it really is!
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics
Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbots.
Fully Automated Fact Checking Using External Sources
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims.
A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates
In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking.
Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums
We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted.
We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!
And we have totally tested it, trust us!
Large-Scale Goodness Polarity Lexicons for Community Question Answering
This leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis.
The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation
Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics.
