no code implementations • RANLP 2021 • Boris Velichkov, Sylvia Vassileva, Simeon Gerginov, Boris Kraychev, Ivaylo Ivanov, Philip Ivanov, Ivan Koychev, Svetla Boytcheva
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.
no code implementations • RANLP 2021 • Sylvia Vassileva, Gergana Todorova, Kristina Ivanova, Boris Velichkov, Ivan Koychev, Galia Angelova, Svetla Boytcheva
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.
no code implementations • RANLP 2021 • Valentin Zmiycharov, Milen Chechev, Gergana Lazarova, Todor Tsonkov, Ivan Koychev
A simple extractive algorithm was selected as a baseline.
1 code implementation • 15 Mar 2024 • Rocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Iliyanov Dimitrov, Ivan Koychev, Preslav Nakov
Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image.
no code implementations • 11 Oct 2023 • Ensiye Kiyamousavi, Boris Kraychev, Ivan Koychev
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.
no code implementations • 13 Sep 2023 • Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov
Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information.
2 code implementations • 4 Jun 2023 • Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Ves Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
1 code implementation • 30 May 2023 • Angel Beshirov, Suzan Hadzhieva, Ivan Koychev, Milena Dobreva
Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes.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)
1 code implementation • 24 May 2023 • Petar Ivanov, Ivan Koychev, Momchil Hardalov, Preslav Nakov
Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers.
1 code implementation • 10 Oct 2022 • Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev, Dmitry Ilvovsky, Preslav Nakov
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.
1 code implementation • 22 Jan 2022 • Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
Testing with quiz questions has proven to be an effective way to assess and improve the educational process.
no code implementations • RANLP 2015 • Todor Mihaylov, Ivan Koychev, Georgi Georgiev, Preslav Nakov
Recently, Web forums have been invaded by opinion manipulation trolls.
no code implementations • SemEval (ACL) 2016 • Tsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva, Todor Mihaylov, Momchil Hardalov, Yasen Kiprov, Daniel Balchev, Ivan Koychev, Preslav Nakov, Ivelina Nikolova, Galia Angelova
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.
no code implementations • RANLP 2021 • Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
In education, open-ended quiz questions have become an important tool for assessing the knowledge of students.
no code implementations • RANLP 2021 • Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev, Maria Castaldo, Tommaso Venturini, Preslav Nakov
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.
2 code implementations • EMNLP 2020 • Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov
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.
3 code implementations • 7 Sep 2020 • Alex Nikolov, Giovanni Da San Martino, Ivan Koychev, Preslav Nakov
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.
no code implementations • 30 Apr 2020 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
no code implementations • 14 Dec 2019 • Pepa Gencheva, Ivan Koychev, Lluís Màrquez, Alberto Barrón-Cedeño, Preslav Nakov
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.
1 code implementation • 19 Nov 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
1 code implementation • 20 Oct 2019 • Yoan Dinkov, Ahmed Ali, Ivan Koychev, Preslav Nakov
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.
no code implementations • 4 Oct 2019 • Daniel Kopev, Ahmed Ali, Ivan Koychev, Preslav Nakov
We present work on deception detection, where, given a spoken claim, we aim to predict its factuality.
no code implementations • RANLP 2019 • Boris Velichkov, Ivan Koychev, Svetla Boytcheva
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.
1 code implementation • IJCNLP 2019 • Dimitrina Zlatkova, Preslav Nakov, Ivan Koychev
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.
1 code implementation • RANLP 2019 • Yoan Dinkov, Ivan Koychev, Preslav Nakov
Online media aim for reaching ever bigger audience and for attracting ever longer attention span.
1 code implementation • RANLP 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.
no code implementations • 17 Jun 2019 • Daniel Kopev, Dimitrina Zlatkova, Kristiyan Mitov, Atanas Atanasov, Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
no code implementations • 12 Feb 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents.
1 code implementation • 2 Sep 2018 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
no code implementations • SEMEVAL 2018 • Daniel Kopev, Atanas Atanasov, Dimitrina Zlatkova, Momchil Hardalov, Ivan Koychev, Ivelina Nikolova, Galia Angelova
We present the system built for SemEval-2018 Task 2 on Emoji Prediction.
1 code implementation • 10 Mar 2018 • Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
So, we did this research on fake news/click-bait detection and trust us, it is totally great research, it really is!
no code implementations • RANLP 2017 • Martin Boyanov, Ivan Koychev, Preslav Nakov, Alessandro Moschitti, Giovanni Da San Martino
Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbots.
1 code implementation • RANLP 2017 • Georgi Karadzhov, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Ivan Koychev
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.
1 code implementation • RANLP 2017 • Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
And we have totally tested it, trust us!
1 code implementation • RANLP 2017 • Pepa Gencheva, Preslav Nakov, Llu{\'\i}s M{\`a}rquez, Alberto Barr{\'o}n-Cede{\~n}o, Ivan Koychev
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.
1 code implementation • RANLP 2017 • Preslav Nakov, Tsvetomila Mihaylova, Llu{\'\i}s M{\`a}rquez, Yashkumar Shiroya, Ivan Koychev
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.
no code implementations • 20 Jul 2017 • Todor Mihaylov, Daniel Belchev, Yasen Kiprov, Ivan Koychev, Preslav Nakov
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.
2 code implementations • 12 Jul 2017 • Georgi Karadjov, Tsvetomila Mihaylova, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
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.