no code implementations • • Jakub Piskorski, Bogdan Babych, Zara Kancheva, Olga Kanishcheva, Maria Lebedeva, Michał Marcińczuk, Preslav Nakov, Petya Osenova, Lidia Pivovarova, Senja Pollak, Pavel Přibáň, Ivaylo Radev, Marko Robnik-Sikonja, Vasyl Starko, Josef Steinberger, Roman Yangarber
Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task.
In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes.
We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods.
This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection.
The way the words are used evolves through time, mirroring cultural or technological evolution of society.
This paper is a part of a collaboration between computer scientists and historians aimed at development of novel tools and methods to improve analysis of historical newspapers.
The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking.
We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification.
We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence.
Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news.
The reported evaluation figures reflect the relatively higher level of complexity of named entity-related tasks in the context of processing texts in Slavic languages.
In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles.