How Do Words Change Meaning over Time?

Many words change meaning over time, and some acquire new senses.

For example, the word ‘tweet’ originally referred to a sound produced by birds, and it has recently acquired a new sense related to a message sent via the social media platform Twitter. It is an essential part of lexicographers’ work to find new senses and define them in dictionaries. But this is a very labour-intensive task. Can we make computers help humans find new senses automatically?

Computational linguistics researchers have been looking at solutions to this challenge for a decade, and this is still an active area of investigation. We would like to share with you a couple of examples of such research, as they are very relevant to our understanding of what is happening in computational lexicography and what we might expect next.

Paul Cook and colleagues published an article in 2014 in which they describe a system for identifying new word senses automatically – you can find the details at the end of this article. These researchers compared the 1995 edition of the Concise Oxford English Dictionary with the 2005 edition, and collected a series of examples of novel senses by hand. They then analysed random usages of each lemma in two English corpora, the British National Corpus (containing British English content from the late 20th century) and the ukWaC corpus (containing webpages with a .uk domain in 2007). The British National Corpus was used as a source of established senses, while the ukWaC corpus was used as a source of new senses. For example, the novel sense of the word domain related to the internet was found in ukWaC, but not in the British National Corpus. If we put the two corpora together, how can we teach a computer to distinguish those containing the original sense of ‘domain’ from those containing the new one?

The idea is to use the context of a word to learn about its meaning, and this approach is very popular in the field of ‘distributional semantics’. This makes intuitive sense because, if we don’t know the meaning of a word in a sentence we can use the words around it to help us. For example, imagine that the word ‘embue’ existed and imagine that you found this sentence:

‘Many embues live in forests and eat nuts.’

References

_{Basile, P. and McGillivray, B. (2018) ‘Exploiting the Web for Semantic Change Detection’. in Proceedings of the 21st International Conference on Discovery Science (DS 2018), Cyprus. Springer-Verlag}

_{Cook, P., Lau, J. H., McCarthy, D., and Baldwin, T. (2014) ‘Novel Word-Sense Identification’. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 1624-1635. available from http://www.aclweb.org/anthology/C14-1154}

_{JISC and the Internet Archive (2013) ‘JISC UK Web Domain Dataset (1996-2013)’. The British Library https://doi.org/10.5259/ukwa.ds.2/1}

_{Oxford Dictionaries (1995) The Concise Oxford Dictionary of Current English. 9th edn. Oxford: Oxford University Press Thompson, D. (ed)}

_{Oxford Dictionaries (2008) The Concise Oxford English Dictionary. 11th edn. Oxford: Oxford University Press Soanes, C. and Stevenson, A. (eds)}