Juraj Vladika


A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider | Tim Schopf | Juraj Vladika | Mikhail Galkin | Elena Simperl | Florian Matthes
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.

TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity Analysis
Juraj Vladika | Stephen Meisenbacher | Florian Matthes
Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text

The task of quantifying the complexity of written language presents an interesting endeavor, particularly in the opportunity that it presents for aiding language learners. In this pursuit, the question of what exactly about natural language contributes to its complexity (or lack thereof) is an interesting point of investigation. We propose a hybrid approach, utilizing shallow models to capture linguistic features, while leveraging a fine-tuned embedding model to encode the semantics of input text. By harmonizing these two methods, we achieve competitive scores in the given metric, and we demonstrate improvements over either singular method. In addition, we uncover the effectiveness of Gaussian processes in the training of shallow models for text complexity analysis.


TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection
Niko Palić | Juraj Vladika | Dominik Čubelić | Ivan Lovrenčić | Maja Buljan | Jan Šnajder
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum. Our system receives an article in its raw, textual form, analyzes it, and predicts with moderate accuracy whether the article is hyperpartisan. The learning model used was primarily trained on a manually prelabeled dataset containing news articles. The system relies on the previously constructed SVM model, available in the Python Scikit-Learn library. We ranked 6th in the competition of 42 teams with an accuracy of 79.1% (the winning team had 82.2%).