The meaning of words changes all the time. Think of the word ‘blackberry’, for example, which has been used for centuries to refer to a fruit. In 1999, a new brand of mobile devices was launched with the name BlackBerry. Suddenly, there was a new way of using this old word. ‘Cloud’ is another example of a well-established word whose association with ‘cloud computing’ only emerged in the past couple of decades. Linguists call this phenomenon ‘semantic change’ and have studied its complex mechanisms for a long time. What has changed in recent years is that we now have access to huge collections of data which can be mined to find these changes automatically. Web archives are a great example of such collections, because they contain a record of the changing content of web pages.

But how can we automatically detect in a huge web archive when a word has changed its meaning? A common strategy is to build geometric representations of words called word embeddings. Word embeddings use lots of data about the context in which words are used so that similar words can be clustered together. We can then do operations on these embeddings, for example to find the words that are closest (and most similar in meaning) to a given word. It’s a useful technique, but building embeddings takes a lot of computing power. Having access to pre-trained embeddings can therefore make a big difference, enabling those in the scientific community without sufficient computational resources to participate in this research.

A team of researchers from The Alan Turing Institute and the Universities of Bari, Oxford and Warwick, in collaboration with the UK Web Archive team based at the British Library, has now released DUKweb, a set of large-scale resources that make pre-trained word embeddings freely available. Described in this article, DUKweb was created from the JISC UK Web Domain Dataset (1996-2013), a collection of all .uk websites archived by the Internet Archive between 1996 and 2013. (This dataset is held and maintained by the UK Web Archive, which has been collecting websites since 2005, initially on a selective basis and since 2013 at a whole domain level.) DUKweb contains 1.3 billion word occurrences and two types of word embeddings for each year of the JISC UK Web Domain Dataset. The size of DUKweb is 330GB.

Researchers can use DUKweb to study semantic change in English between 1996 and 2013, looking at, for instance, the effects of the growth of the internet and social media on word meanings. For example, if the word ‘blackberry’ is used mostly to refer to fruits in 1996 and to mobile phones in 2000, the 1996 embedding for this word will be quite different from its 2000 embedding. In this way, we can find words that may have changed meaning in this time period.

The figure below (from Tsakalidis et al., 2019) shows four words whose contexts of use have changed in the last couple of decades: ‘blackberry’, ‘cloud’, ‘eta’ and ‘follow’.

A bar chart showing the semantic shift of 'blackberry', 'cloud', 'eta' and 'follow'
Semantic change of 'blackbery', 'cloud', 'eta' and 'follow'. The bars indicate words most similar to these four words in 2000 (red bars) and in 2013 (blue bars). The scale along the bottom gives a measure of the change.

The resources that underpin DUKweb are hosted on the British Library’s research repository, and are available for anyone in the world to download, reuse and repurpose for their own projects. This repository is part of the BL’s Shared Research Repository for cultural heritage organisations, which brings together the research outputs produced by participating institutions, and makes them discoverable to anybody with an internet connection. Providing a stable, dedicated location to hold heritage datasets in order to share them with a wider research community has been one of the key drivers in the implementation and development of this repository service. We are grateful to the British Library’s Repository Services team for supporting this collaboration between the UK Web Archive team and the Turing by making the content for DUKweb available.

Read the paper:
DUKweb: diachronic word representations from the UK Web Archive corpus

 

Top image: Jeremy Bezanger / Unsplash