Lev Kharlashkin,

Also published as: Lev Kharlashkin


2025

pdf bib
Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages
Mika Hämäläinen | Michael Rießler | Eiaki V. Morooka | Lev Kharlashkin
Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages

pdf bib
ORACLE: Time-Dependent Recursive Summary Graphs for Foresight on News Data Using LLMs
Lev Kharlashkin | Eiaki V. Morooka | Yehor Tereschenko | Mika Hämäläinen
Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages

ORACLE turns daily news into week-over-week, decision-ready insights for one of the Finnish University of Applied Sciences. The platform crawls and versions news, applies University-specific relevance filtering, embeds content, classifies items into PESTEL dimensions and builds a concise Time-Dependent Recursive Summary Graph (TRSG): two clustering layers summarized by an LLM and recomputed weekly. A lightweight change detector highlights what is new, removed or changed, then groups differences into themes for PESTEL-aware analysis. We detail the pipeline, discuss concrete design choices that make the system stable in production and present a curriculum-intelligence use case with an evaluation plan.

2024

pdf bib
Scaling Sustainable Development Goal Predictions across Languages: From English to Finnish
Melany Macias | Lev Kharlashkin, | Leo Huovinen | Mika Hämäläinen
Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages

In this paper, we leverage an exclusive English dataset to train diverse multilingual classifiers, investigating their efficacy in adapting to Finnish data. We employ an exclusively English classification dataset of UN Sustainable Development Goals (SDG) in an education context, to train various multilingual classifiers and examine how well these models can adapt to recognizing the same classes within Finnish university course descriptions. It’s worth noting that Finnish, with a mere 5 million native speakers, presents a significantly less-resourced linguistic context compared to English. The best performing model in our experiments was mBART with an F1-score of 0.843.

pdf bib
Empowering Teachers with Usability-Oriented LLM-Based Tools for Digital Pedagogy
Melany Vanessa Macias | Lev Kharlashkin | Leo Einari Huovinen | Mika Hämäläinen
Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities

We present our work on two LLM-based tools that utilize artificial intelligence and creative technology to improve education. The first tool is a Moodle AI plugin, which helps teachers manage their course content more efficiently using AI-driven analysis, content generation, and an interactive chatbot. The second one is a curriculum planning tool that provides insight into the sustainability, work-life relevance, and workload of each course. Both of these tools have the common goal of integrating sustainable development goals (UN SDGs) into teaching, among other things. We will describe the usability-focused and user-centric approach we have embraced when developing these tools.