Search Results for author: Steffen Remus

Found 15 papers, 3 papers with code

Language Models Explain Word Reading Times Better Than Empirical Predictability

no code implementations2 Feb 2022 Markus J. Hofmann, Steffen Remus, Chris Biemann, Ralph Radach, Lars Kuchinke

(3) In recurrent neural networks (RNNs), the subsymbolic units are trained to predict the next word, given all preceding words in the sentences.

Additive models Retrieval +3

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

no code implementations LREC 2020 Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko

We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages.

Word Embeddings Word Sense Disambiguation

LT Expertfinder: An Evaluation Framework for Expert Finding Methods

1 code implementation NAACL 2019 Tim Fischer, Steffen Remus, Chris Biemann

Particularly for dynamic systems, where topics are not predefined but formulated as a search query, we believe a more informative approach is to perform user studies for directly comparing different methods in the same view.

Information Retrieval Retrieval

Domain-Specific Corpus Expansion with Focused Webcrawling

no code implementations LREC 2016 Steffen Remus, Chris Biemann

This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling.

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