no code implementations • EACL 2017 • Lloren{\c{c}} Escoter, Lidia Pivovarova, Mian Du, Anisia Katinskaia, Roman Yangarber
In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles.
no code implementations • WS 2017 • Kseniya Buraya, Lidia Pivovarova, Sergey Budkov, Andrey Filchenkov
This work deals with ontology learning from unstructured Russian text.
no code implementations • WS 2017 • Jakub Piskorski, Lidia Pivovarova, Jan {\v{S}}najder, Josef Steinberger, Roman Yangarber
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.
no code implementations • WS 2017 • Andrey Kutuzov, Elizaveta Kuzmenko, Lidia Pivovarova
This paper presents a method of automatic construction extraction from a large corpus of Russian.
no code implementations • SEMEVAL 2017 • Lidia Pivovarova, Lloren{\c{c}} Escoter, Arto Klami, Roman Yangarber
Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news.
no code implementations • NAACL 2018 • Lidia Pivovarova, Arto Klami, Roman Yangarber
We address the problem of determining entity-oriented polarity in business news.
no code implementations • SEMEVAL 2018 • Dmitry Kravchenko, Lidia Pivovarova
We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence.
no code implementations • WS 2018 • Lidia Pivovarova, Roman Yangarber
We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification.
no code implementations • WS 2019 • Jakub Piskorski, Laska Laskova, Micha{\l} Marci{\'n}czuk, Lidia Pivovarova, Pavel P{\v{r}}ib{\'a}{\v{n}}, Josef Steinberger, Roman Yangarber
The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking.
no code implementations • RANLP 2019 • Lidia Pivovarova, Elaine Zosa, Jani Marjanen
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.
no code implementations • 18 Jan 2020 • Matej Martinc, Syrielle Montariol, Elaine Zosa, Lidia Pivovarova
The way the words are used evolves through time, mirroring cultural or technological evolution of society.
no code implementations • LREC 2020 • Elaine Zosa, Mark Granroth-Wilding, Lidia Pivovarova
We address the problem of linking related documents across languages in a multilingual collection.
no code implementations • 20 Nov 2020 • Jani Marjanen, Elaine Zosa, Simon Hengchen, Lidia Pivovarova, Mikko Tolonen
This paper addresses methodological issues in diachronic data analysis for historical research.
no code implementations • SEMEVAL 2020 • Matej Martinc, Syrielle Montariol, Elaine Zosa, Lidia Pivovarova
This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection.
1 code implementation • NAACL 2021 • Syrielle Montariol, Matej Martinc, Lidia Pivovarova
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.
no code implementations • ACL (LChange) 2021 • Andrey Kutuzov, Lidia Pivovarova
We present a manually annotated lexical semantic change dataset for Russian: RuShiftEval.
1 code implementation • CoNLL (EMNLP) 2021 • Mario Giulianelli, Andrey Kutuzov, Lidia Pivovarova
Semantics, morphology and syntax are strongly interdependent.
no code implementations • LChange (ACL) 2022 • Mario Giulianelli, Andrey Kutuzov, Lidia Pivovarova
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.
1 code implementation • COLING 2022 • Elaine Zosa, Lidia Pivovarova
This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space.
no code implementations • EACL (BSNLP) 2021 • 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.
1 code implementation • SemEval (NAACL) 2022 • Elaine Zosa, Emanuela Boros, Boshko Koloski, Lidia Pivovarova
In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity).
no code implementations • LREC 2022 • Matej Martinc, Syrielle Montariol, Lidia Pivovarova, Elaine Zosa
We tackle the problem of neural headline generation in a low-resource setting, where only limited amount of data is available to train a model.