Active learning is a technique that helps to minimize the annotation budget required for the
creation of a labeled dataset while maximizing the performance of a model trained on this …

In this paper, we attempted to find speech features of different reactions to frustration to
detect and classify them in social media texts. Frustration is a highly motivated situation in …

This article discusses the importance of automatic schema knowledge extraction for natural
language processing. A broad overview of methods of and approaches to describing and …

This thesis addresses the challenge of evaluating candidate logical formulas, with a specific
focus on axioms, by synergistically combining machine learning with symbolic reasoning …

В статье обсуждается важность автоматического сценарного анализа для понимания
текстов на естественном языке. Дан широкий обзор методов и подходов к описанию и …

Cette thèse aborde le défi de l'évaluation des formules logiques candidates, avec un accent
particulier sur les axiomes, en combinant de manière synergique l'apprentissage …

В монографии рассмотрены проблемы разноуровневой обработки естественного
языка. Представлены методы семантического и дискурсивного анализа текстов на …

In this paper, we present the first gold-standard corpus of Russian noun compounds
annotated with compositionality information. We used Universal Dependency treebanks to …

Active learning _ BIBM 2019 _ Philips Page 1 Deep Active Learning: Reducing Annotation
Effort for Automatic Sequence Tagging of Clinical and Biomedical Texts Dr. Artem Shelmanov …