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

Hätty, Anna; Schlechtweg, Dominik; Walde, Sabine Schulte im

doi:10.18653/v1/s19-1001

Cited by 12 publications

(11 citation statements)

References 21 publications

(19 reference statements)

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“…We compare our proposed method (NN) to the method of Hamilton et al (2016b) described in Section 4 (AlignCos), in which the vector spaces are first aligned using the OP algorithm, and then words are ranked according to the cosine-distance between the word representation in the two spaces. 5 This method was shown to outperform all others that were compared to it by Schlechtweg et al (2019).…”

Section: Methodsmentioning

confidence: 94%

“…This field of semantic change suffers from lack of proper evaluation datasets, and there is no common benchmark that is being used. Two new datasets were recently introduced, and used to extensively compare between previous methods (Schlechtweg et al, 2019): the DURel dataset (Schlechtweg et al, 2018) focuses on diachronic changes, while the SURel dataset (Hätty et al, 2019) focuses on domain-based semantic changes. We use them to verify the quality of our results and compare against AlignCos (Hamilton et al, 2016b).…”

Section: Quantitative Evaluation: Durel and Surel Datasetsmentioning

confidence: 99%

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Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora

Gonen¹,

Jawahar²,

Seddah³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science. This is commonly approached by training word embeddings on each corpus, aligning the vector spaces, and looking for words whose cosine distance in the aligned space is large. However, these methods often require extensive filtering of the vocabulary to perform well, and-as we show in this work-result in unstable, and hence less reliable, results. We propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. The method is simple, interpretable and stable. We demonstrate its effectiveness in 9 different setups, considering different corpus splitting criteria (age, gender and profession of tweet authors, time of tweet) and different languages (English, French and Hebrew).

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Section: Methodsmentioning

confidence: 94%

Section: Quantitative Evaluation: Durel and Surel Datasetsmentioning

confidence: 99%

Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora

Gonen¹,

Jawahar²,

Seddah³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…A simple improvement of approach in [53] would be to use contextualized embedding models pre-trained on domain-specific collections such as SciBERT that were trained on scholarly corpora [68]. Another one is to consider specialized term extraction techniques such as ones based on recognizing meaning shifts between general and domain-specific language [69]. In our work we further extend change-based scholarly data summarization approaches based on these and further ideas.…”

Section: Temporal Analysis Of Scholarly Datamentioning

confidence: 99%

Change-Oriented Summarization of Temporal Scholarly Document Collections by Semantic Evolution Analysis

2022

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The number of scholarly publications has dramatically increased over the last decades. For anyone new to a particular science domain it is not easy to understand the major trends and significant changes that the domain has undergone over time. Temporal summarization and related approaches should be then useful to make sense of scholarly temporal collections. In this paper we demonstrate an approach to analyze the dataset of research papers by providing a high level overview of important changes that occurred over time in this dataset. The novelty of our approach lies in the adaptation of methods used for semantic term evolution analysis. However, we analyze not just semantic evolution of single words independently, but we estimate common semantic drifts shared by groups of semantically converging words. As an example dataset we use the ACL Anthology Reference Corpus that spans from 1974 to 2015 and contains 22,878 scholarly articles.

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“…Biemann, 2013). Studies on graded word meaning, however, cover only small amounts of data (Soares da Silva, 1992;Brown, 2008;McCarthy and Navigli, 2009;Erk et al, 2009Erk et al, , 2013Hätty et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages

Schlechtweg

Tahmasebi

Hengchen

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. We describe in detail the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible -diachronic and synchronic -uses for this dataset.

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

Cited by 12 publications

References 21 publications

Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora

Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora

Change-Oriented Summarization of Temporal Scholarly Document Collections by Semantic Evolution Analysis

DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages

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