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20192026
most citedBayesian Methods for Semi-supervised Text Annotation

6 citations · 6 across the 4 of their papers we have counts for

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cs.CL2026

Challenges in Explaining Pretrained Clinical Text Classifiers

Kristian Miok, Matej Klemen, Blaz Škrlj +1

Explaining the predictions of neural models in clinical NLP remains a significant challenge, especially for complex tasks involving long, unstructured medical texts. While post-hoc…

cs.CL2025

TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning

Kristian Miok, Blaz Škrlj, Daniela Zaharie +1

Clinical language models often struggle to provide trustworthy predictions and explanations when applied to lengthy, unstructured electronic health records (EHRs). This work introd…

cs.CL2022

Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

Kristian Miok, Encarnacion Hidalgo-Tenorio, Petya Osenova +2

Parliamentary and legislative debate transcripts provide informative insight into elected politicians' opinions, positions, and policy preferences. They are interesting for politic…

cs.CL2020★ 6 cited

Bayesian Methods for Semi-supervised Text Annotation

Kristian Miok, Gregor Pirs, Marko Robnik-Sikonja

Human annotations are an important source of information in the development of natural language understanding approaches. As under the pressure of productivity annotators can assig…

cs.CL2019

Prediction Uncertainty Estimation for Hate Speech Classification

Kristian Miok, Dong Nguyen-Doan, Blaž Škrlj +2

As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large comm…