DUKweb: Diachronic word representations from the UK Web Archive corpus

TL;DR

DUKweb provides large-scale, time-sensitive word representations derived from UK web data (1996-2013), enabling diachronic semantic change analysis with high-quality resources for NLP and social studies.

Contribution

This paper introduces DUKweb, a comprehensive set of diachronic word embeddings and co-occurrence matrices from UK web data, filling a resource gap for temporal semantic analysis.

Findings

Demonstrated the usefulness of DUKweb in detecting lexical semantic change.

Showcased the quality and potential reuse of the resources.

Provided a case study validating the approach.

Abstract

Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard resource for this task. However, given the significant computational resources needed for their generation, very few resources exist that make diachronic word embeddings available to the scientific community. In this paper we present DUKweb, a set of large-scale resources designed for the diachronic analysis of contemporary English. DUKweb was created from the JISC UK Web Domain Dataset (1996-2013), a very large archive which collects resources from the Internet Archive that were hosted on domains ending in `.uk'. DUKweb consists of a series word co-occurrence matrices and two types of word embeddings for each year in the JISC UK Web Domain dataset. We show the reuse potential of DUKweb and its quality standards via a case study on word meaning change detection.