1 code implementation • 29 Apr 2024 • Francesco Periti, Pierluigi Cassotti, Haim Dubossarsky, Nina Tahmasebi
Furthermore, we leverage the replacement schema as a basis for a novel \textit{interpretable} model for semantic change.
1 code implementation • 19 Feb 2024 • Francesco Periti, Nina Tahmasebi
Our evaluation is performed across different languages on eight available benchmarks for LSC, and shows that (i) APD outperforms other approaches for GCD; (ii) XL-LEXEME outperforms other contextualized models for WiC, WSI, and GCD, while being comparable to GPT-4; (iii) there is a clear need for improving the modeling of word meanings, as well as focus on how, when, and why these meanings change, rather than solely focusing on the extent of semantic change.
1 code implementation • 25 Jan 2024 • Francesco Periti, Haim Dubossarsky, Nina Tahmasebi
In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems.
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.
no code implementations • 13 Apr 2023 • Nina Tahmasebi, Haim Dubossarsky
In this chapter we provide an overview of computational modeling for semantic change using large and semi-large textual corpora.
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 • NoDaLiDa 2021 • Simon Hengchen, Nina Tahmasebi
Language models are notoriously difficult to evaluate.
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.
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.
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 • 15 Nov 2018 • Nina Tahmasebi, Lars Borin, Adam Jatowt
In this article we focus on diachronic conceptual change as an extension of semantic change.
no code implementations • RANLP 2017 • Sallam Abualhaija, Nina Tahmasebi, Diane Forin, Karl-Heinz Zimmermann
Word sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications.
no code implementations • RANLP 2017 • Nina Tahmasebi, Thomas Risse
We present a method for detecting word sense changes by utilizing automatically induced word senses.
no code implementations • 3 Feb 2017 • Helge Holzmann, Nina Tahmasebi, Thomas Risse
We approach these by adapting an existing NEER method to work on noisy data like the Web and the Blogosphere in particular.