Papers by Daniel Baumartz
LTV: Labeled Topic Vector (C18-2)
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| Challenge: | Using nnDDC, we generate labeled topic classifications based on the Dewey Decimal Classification (DDC) Unlike related approaches, we use classifiers to define the dimensions of CISS, which are directly labeles by the underlying target class. |
| Approach: | They propose a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC) they propose nnDDC, a largely language-independent natural network-based classifier for DDC, which is language-dependent . |
| Outcome: | The proposed model is language-independent and performs well in 40 languages. |
FastSense: An Efficient Word Sense Disambiguation Classifier (L18-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a task that is often overlooked by NLP pipelines because of its complexity and complexity. |
| Approach: | They propose a neural network-based tool for word sense disambiguation called fastSense. |
| Outcome: | The proposed tool can process huge amounts of data quickly and surpasses state-of-the-art tools in terms of F-measure. |
Unlocking the Heterogeneous Landscape of Big Data NLP with DUUI (2023.findings-emnlp)
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| Challenge: | Automated analysis of large corpora is a complex task, especially in terms of time efficiency. |
| Approach: | They propose a framework for automatic distributed analysis of text corpora that leverages Big Data experience and virtualization with Docker. |
| Outcome: | The proposed framework is scalable, flexible, lightweight, and feature-rich for automatic distributed analysis of text corpora. |
Dependencies over Times and Tools (DoTT) (2024.lrec-main)
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Andy Luecking, Giuseppe Abrami, Leon Hammerla, Marc Rahn, Daniel Baumartz, Steffen Eger, Alexander Mehler
| Challenge: | Using the examples of English and German, we examine how parsers trained on modern variants of these languages can be transferred to older language levels without loss. |
| Approach: | They develop a treebank of diachronic corpora enriched with dependency annotations using 3 parsers, 6 pre-trained language models, 5 newly trained models for German, and two tag sets. |
| Outcome: | The proposed treebank covers the time period from 1800 until today and is based on the DependencyAnnotator annotation tool. |