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Challenges for Computational Lexical Semantic Change ...
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Abstract:
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. Most of the research so far has focused on methods for modelling and detecting semantic change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions. ... : To appear in: Nina Tahmasebi, Lars Borin, Adam Jatowt, Yang Xu, Simon Hengchen (eds). Computational Approaches to Semantic Change. Berlin: Language Science Press. [preliminary page numbering] ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2101.07668 https://arxiv.org/abs/2101.07668
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SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection ...
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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection ...
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Swedish Test Data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection ...
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LatinISE test data for SemEval 2020 task 1 with additional token versions of the corpora ...
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LatinISE test data for SemEval 2020 task 1 with additional token versions of the corpora ...
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Swedish Test Data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection ...
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