Search Results for author: Eugen Ruppert

Found 9 papers, 1 papers with code

Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations

no code implementations LREC 2020 {\"O}zge Alacam, Eugen Ruppert, Amr Rekaby Salama, Tobias Staron, Wolfgang Menzel

Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener.

Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter

no code implementations7 Nov 2018 Gregor Wiedemann, Eugen Ruppert, Raghav Jindal, Chris Biemann

Best results are achieved from pre-training our model on the unsupervised topic clustering of tweets in combination with thematic user cluster information.

General Classification Transfer Learning

Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

no code implementations LREC 2018 Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann

We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7. 5 billion of named entity occurrences in 14. 3 billion sentences from a web-scale crawl of the \textsc{Common Crawl} project.

Open Information Extraction Question Answering +1

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

1 code implementation EMNLP 2017 Alexander Panchenko, Fide Marten, Eugen Ruppert, Stefano Faralli, Dmitry Ustalov, Simone Paolo Ponzetto, Chris Biemann

In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.

Word Sense Disambiguation

Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation

no code implementations EACL 2017 Alex Panchenko, er, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann

On the example of word sense induction and disambiguation (WSID), we show that it is possible to develop an interpretable model that matches the state-of-the-art models in accuracy.

Word Embeddings Word Sense Induction

Cannot find the paper you are looking for? You can Submit a new open access paper.