no code implementations • EACL (VarDial) 2021 • Diego Frassinelli, Gabriella Lapesa, Reem Alatrash, Dominik Schlechtweg, Sabine Schulte im Walde
Kiezdeutsch is a variety of German predominantly spoken by teenagers from multi-ethnic urban neighborhoods in casual conversations with their peers.
no code implementations • LChange (ACL) 2022 • Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg
We present the first shared task on semantic change discovery and detection in Spanish.
no code implementations • LREC 2022 • Gioia Baldissin, Dominik Schlechtweg, Sabine Schulte im Walde
We provide a novel dataset – DiaWUG – with judgements on diatopic lexical semantic variation for six Spanish variants in Europe and Latin America.
no code implementations • 26 Mar 2024 • Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev, Dominik Schlechtweg
We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions.
no code implementations • 4 Mar 2024 • Jonathan Lautenschlager, Emma Sköldberg, Simon Hengchen, Dominik Schlechtweg
This study addresses the task of Unknown Sense Detection in English and Swedish.
no code implementations • 21 Nov 2023 • Dominik Schlechtweg, Shafqat Mumtaz Virk, Pauline Sander, Emma Sköldberg, Lukas Theuer Linke, Tuo Zhang, Nina Tahmasebi, Jonas Kuhn, Sabine Schulte im Walde
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface.
1 code implementation • 13 May 2022 • Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg
We present the first shared task on semantic change discovery and detection in Spanish and create the first dataset of Spanish words manually annotated for semantic change using the DURel framework (Schlechtweg et al., 2018).
1 code implementation • Joint Conference on Lexical and Computational Semantics 2021 • Dominik Schlechtweg, Enrique Castaneda, Jonas Kuhn, Sabine Schulte im Walde
We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences.
1 code implementation • ACL 2021 • Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde
While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models.
1 code implementation • Findings (ACL) 2021 • Thomas Bott, Dominik Schlechtweg, Sabine Schulte im Walde
This paper presents a comparison of unsupervised methods of hypernymy prediction (i. e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym).
1 code implementation • EMNLP 2021 • Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray
Word meaning is notoriously difficult to capture, both synchronically and diachronically.
no code implementations • EACL 2021 • Severin Laicher, Sinan Kurtyigit, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde
Type- and token-based embedding architectures are still competing in lexical semantic change detection.
1 code implementation • EACL 2021 • Jens Kaiser, Sinan Kurtyigit, Serge Kotchourko, Dominik Schlechtweg
Lexical semantic change detection is a new and innovative research field.
no code implementations • 19 Jan 2021 • Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg, Haim Dubossarsky
The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics.
1 code implementation • 14 Nov 2020 • Severin Laicher, Gioia Baldissin, Enrique Castañeda, Dominik Schlechtweg, Sabine Schulte im Walde
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian.
no code implementations • 6 Nov 2020 • Jens Kaiser, Dominik Schlechtweg, Sabine Schulte im Walde
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian.
no code implementations • SEMEVAL 2020 • Jens Kaiser, Dominik Schlechtweg, Sean Papay, Sabine Schulte im Walde
We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling.
2 code implementations • SEMEVAL 2020 • Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, Nina Tahmasebi
Lexical Semantic Change detection, i. e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics.
no code implementations • ACL 2020 • Anna H{\"a}tty, Dominik Schlechtweg, Michael Dorna, Sabine Schulte im Walde
While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied.
no code implementations • LREC 2020 • Reem Alatrash, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde
Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics.
no code implementations • 21 Jan 2020 • Adnan Ahmad, Kiflom Desta, Fabian Lang, Dominik Schlechtweg
Recent NLP architectures have illustrated in various ways how semantic change can be captured across time and domains.
no code implementations • 9 Jan 2020 • Dominik Schlechtweg, Sabine Schulte im Walde
We present a novel procedure to simulate lexical semantic change from synchronic sense-annotated data, and demonstrate its usefulness for assessing lexical semantic change detection models.
1 code implementation • ACL 2019 • Dominik Schlechtweg, Anna Hätty, Marco del Tredici, Sabine Schulte im Walde
We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains.
1 code implementation • WS 2019 • Dominik Schlechtweg, Cennet Oguz, Sabine Schulte im Walde
We simulate first- and second-order context overlap and show that Skip-Gram with Negative Sampling is similar to Singular Value Decomposition in capturing second-order co-occurrence information, while Pointwise Mutual Information is agnostic to it.
1 code implementation • ACL 2019 • Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg
State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment.
no code implementations • SEMEVAL 2019 • Anna H{\"a}tty, Dominik Schlechtweg, Sabine Schulte im Walde
We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts.
no code implementations • NAACL 2018 • Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann
We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models of lexical semantic change.
no code implementations • 14 Apr 2018 • Dominik Schlechtweg, Sabine Schulte im Walde
We test the hypothesis that the degree of grammaticalization of German prepositions correlates with their corpus-based contextual dispersion measured by word entropy.
1 code implementation • CONLL 2017 • Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus, Sabine Schulte im Walde, Daniel Hole
This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change.
1 code implementation • EACL 2017 • Vered Shwartz, Enrico Santus, Dominik Schlechtweg
The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution.
Ranked #7 on Hypernym Discovery on Music domain
no code implementations • LREC 2016 • Dominik Schlechtweg
It is because of this convenience that most current state-of-the-art-models of distributional semantics operate on raw text, although there have been successful attempts to integrate other kinds of―e. g., syntactic―information to improve distributional semantic models.