Diachronic Usage Relatedness (DURel): A Framework for the Annotation
            of Lexical Semantic Change

Schlechtweg, Dominik; Walde, Sabine Schulte im; Eckmann, Stefanie

doi:10.18653/v1/n18-2027

Cited by 62 publications

(122 citation statements)

References 13 publications

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“…The datasets contain words annotated similar to the DURel framework [38] according to the intensity of the semantic changes they have undergone. Each word was presented to 5 independent annotators along with two context sentences.…”

Section: Evaluation Datasetmentioning

confidence: 99%

ELMo and BERT in Semantic Change Detection for Russian

Rodina

Trofimova

Kutuzov

et al. 2021

Lecture Notes in Computer Science

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We study the effectiveness of contextualized embeddings for the task of diachronic semantic change detection for Russian language data. Evaluation test sets consist of Russian nouns and adjectives annotated based on their occurrences in texts created in pre-Soviet, Soviet and post-Soviet time periods. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their semantic change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.

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Section: Evaluation Datasetmentioning

confidence: 99%

ELMo and BERT in Semantic Change Detection for Russian

Rodina

Trofimova

Kutuzov

et al. 2021

Lecture Notes in Computer Science

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“…Evaluating models tackling lexical semantic change is notoriously challenging. Frameworks are either lacking or focus on very specific types of sense change (Schlechtweg et al, 2018;. Exceptions are Kulkarni et al (2015), Basile and McGillivray (2018) and Hamilton et al (2016), who focus on the change points of word senses.…”

Section: Evaluation Frameworkmentioning

confidence: 99%

GASC: Genre-Aware Semantic Change for Ancient Greek

Perrone¹,

Palma²,

Hengchen³

et al. 2019

Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

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Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document timestamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts' genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.

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“…3 The Dataset: SURel 1 Dataset Creation SURel was created analogously to DURel (Schlechtweg et al, 2018), a dataset for meaning shifts across time. Our novel dataset comprises a manual annotation of meaning relatedness between uses of target words in a general-language and a domain-specific corpus.…”

Section: Meaning Shifts In Terminologymentioning

confidence: 99%

“…: SURel dataset. MRS (mean relatedness score) denotes the compare rank as described in (Schlechtweg et al, 2018), where high values denote low change. Translations are illustrative for possible meaning shifts, while further senses might exist.…”

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confidence: 99%

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SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction

Hätty

Schlechtweg

Walde

2019

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

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We introduce SURel, a novel dataset for German with human-annotated meaning shifts between general-language and domain-specific contexts. We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors. Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts.

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Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change

Cited by 62 publications

References 13 publications

ELMo and BERT in Semantic Change Detection for Russian

ELMo and BERT in Semantic Change Detection for Russian

GASC: Genre-Aware Semantic Change for Ancient Greek

SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction

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