Search Results for author: Ivan Koychev

Found 40 papers, 20 papers with code

Automatic Transformation of Clinical Narratives into Structured Format

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.

Binary Classification Negation +1

Comparative Analysis of Fine-tuned Deep Learning Language Models for ICD-10 Classification Task for Bulgarian Language

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.

FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning

no code implementations11 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.

Benchmarking Diversity +1

bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark

2 code implementations4 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.

Fact Checking named-entity-recognition +5

DuoSearch: A Novel Search Engine for Bulgarian Historical Documents

1 code implementation30 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Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes.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)

Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data

1 code implementation24 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.

Fact Checking Misinformation

CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media

1 code implementation10 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.

Fact Checking

Leaf: Multiple-Choice Question Generation

1 code implementation22 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.

Multiple-choice Question Answering +2

Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels

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.

EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering

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.

Question Answering Transfer Learning

Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models

3 code implementations7 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.

Fact Checking Misinformation

Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection

no code implementations30 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.

Intent Detection Natural Language Understanding +2

A Context-Aware Approach for Detecting Check-Worthy Claims in Political Debates

no code implementations14 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.

Fact Checking

In Search of Credible News

1 code implementation19 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.

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

1 code implementation20 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.

Bias Detection Multimodal Deep Learning

Deep learning contextual models for prediction of sport event outcome from sportsman's interviews

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.

Fact-Checking Meets Fauxtography: Verifying Claims About Images

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.

Fact Checking

Detecting Toxicity in News Articles: Application to Bulgarian

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.

Recursive Style Breach Detection with Multifaceted Ensemble Learning

no code implementations17 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.

Change Detection Ensemble Learning +1

Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots

no code implementations12 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.

Information Retrieval Language Modelling +4

Towards Automated Customer Support

1 code implementation2 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.

Information Retrieval Machine Translation +3

We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!

1 code implementation10 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!

Fully Automated Fact Checking Using External Sources

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.

Community Question Answering Fact Checking

Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums

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.

Community Question Answering Information Retrieval

A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates

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.

Fact Checking

Large-Scale Goodness Polarity Lexicons for Community Question Answering

no code implementations20 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.

Community Question Answering Sentiment Analysis

The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation

2 code implementations12 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.

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