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Found 9 papers, 1 papers with code
Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations
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.
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection
We present a neural network based approach of transfer learning for offensive language detection.
Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter
Best results are achieved from pre-training our model on the unsupervised topic clustering of tweets in combination with thematic user cluster information.
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
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.
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
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.
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
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.
