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Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Exploiting the Web for Semantic Change Detection

Authors : Pierpaolo Basile, Barbara McGillivray

Published in: Discovery Science

Publisher: Springer International Publishing

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Abstract

Detecting significant linguistic shifts in the meaning and usage of words has gained more attention over the last few years. Linguistic shifts are especially prevalent on the Internet, where words’ meaning can change rapidly. In this work, we describe the construction of a large diachronic corpus that relies on the UK Web Archive and we propose a preliminary analysis of semantic change detection exploiting a particular technique called Temporal Random Indexing. Results of the evaluation are promising and give us important insights for further investigations.

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Footnotes
4
V contains the terms that we want to analyse, typically, the most n frequent terms.
 
5
We did not highlight orange in the list because in this context it could refer to the fruit, in which case it would be related to the fruit sense of blackberry, or to the mobile phone company, in which case it would be related to the cellphone sense of blackberry.
 
6
As the earliest texts in the corpus date from 1996, we allowed for a one-year buffer between this date and the date of first usage according to the OED, under the assumption that a sense first recorded in the OED in 1995 could be recorded with sufficient evidence in our corpus at least one year later.
 
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Metadata
Title
Exploiting the Web for Semantic Change Detection
Authors
Pierpaolo Basile
Barbara McGillivray
Copyright Year
2018
Book
Discovery Science

Print ISBN: 978-3-030-01770-5

Electronic ISBN: 978-3-030-01771-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01771-2_13

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