activity
20222026
most citedAre We Really Making Much Progress in Text Classification? A Comparative Review

10 citations · 18 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook

Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa +1

Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approa…

cs.CL2025

CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents

Fabian Karl, Ansgar Scherp

Publication databases rely on accurate metadata extraction from diverse web sources, yet variations in web layouts and data formats present challenges for metadata providers. This…

cs.CL2024

Efficient Continual Learning for Small Language Models with a Discrete Key-Value Bottleneck

Andor Diera, Lukas Galke, Fabian Karl +1

Continual learning remains a challenge across various natural language processing (NLP) tasks, as models updated with new training data often risk catastrophic forgetting of previo…

cs.CL2023

GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding

Andor Diera, Abdelhalim Dahou, Lukas Galke +3

Language models can serve as a valuable tool for software developers to increase productivity. Large generative models can be used for code generation and code completion, while sm…

cs.CL2022★ 8 cited

Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets

Fabian Karl, Ansgar Scherp

Short text classification is a crucial and challenging aspect of Natural Language Processing. For this reason, there are numerous highly specialized short text classifiers. However…

cs.CL2022★ 10 cited

Are We Really Making Much Progress in Text Classification? A Comparative Review

Lukas Galke, Ansgar Scherp, Andor Diera +5

We survey the literature on single-label, multi-label, and hierarchical text classification and provide a quantitative comparison of methods categorized into bag-of-words, sequence…