Search Results for author: Steffen Remus

Found 13 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 Semantic Similarity +2

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

Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings

1 code implementation23 Sep 2019 Gregor Wiedemann, Steffen Remus, Avi Chawla, Chris Biemann

Since vectors of the same word type can vary depending on the respective context, they implicitly provide a model for word sense disambiguation (WSD).

General Classification Translation +1

Hierarchical Multi-label Classification of Text with Capsule Networks

1 code implementation ACL 2019 Rami Aly, Steffen Remus, Chris Biemann

Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference.

Classification General Classification +3

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

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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