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20162021
most citedChallenges for Computational Lexical Semantic Change

15 citations · 42 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CL20215 cited

SuperSim: a test set for word similarity and relatedness in Swedish

Simon Hengchen, Nina Tahmasebi

Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The tes…

cs.CL20216 cited

Lexical semantic change for Ancient Greek and Latin

Valerio Perrone, Simon Hengchen, Marco Palma +3

Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associatin…

cs.CL202115 cited

Challenges for Computational Lexical Semantic Change

Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg +1

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 an…

cs.CL20202 cited

Topic modelling discourse dynamics in historical newspapers

Jani Marjanen, Elaine Zosa, Simon Hengchen +2

This paper addresses methodological issues in diachronic data analysis for historical research. We apply two families of topic models (LDA and DTM) on a relatively large set of his…

cs.CL202012 cited

An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish

Quan Duong, Mika Hämäläinen, Simon Hengchen

Historical corpora are known to contain errors introduced by OCR (optical character recognition) methods used in the digitization process, often said to be degrading the performanc…

cs.CL20202 cited

SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen +2

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 li…