641 citations · 1k across the 33 of their papers we have counts for
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Analysis of the Ethiopic Twitter Dataset for Abusive Speech in Amharic
Seid Muhie Yimam, Abinew Ali Ayele, Chris Biemann
In this paper, we present an analysis of the first Ethiopic Twitter Dataset for the Amharic language targeted for recognizing abusive speech. The dataset has been collected since 2…
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
Gregor Wiedemann, Steffen Remus, Avi Chawla +1
Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in…
Making Fast Graph-based Algorithms with Graph Metric Embeddings
Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk +2
The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs. We explore dense vector representations as an effective way to appro…
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
Max Friedrich, Arne Köhn, Gregor Wiedemann +1
De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHRs) to be shared for…
Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings
Rami Aly, Shantanu Acharya, Alexander Ossa +3
We introduce the use of Poincaré embeddings to improve existing state-of-the-art approaches to domain-specific taxonomy induction from text as a signal for both relocating wrong hy…
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Saba Anwar, Dmitry Ustalov, Nikolay Arefyev +3
We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsuperv…