HumAI: Is more data always better? Corpora, big data, and AI for the study of language

About the seminar

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In recent decades, linguistics has increasingly relied on corpora to study language. As more texts have become digitally available, corpora have grown dramatically in size, and datasets containing billions of words are now common. Such big data allow linguists to investigate infrequent phenomena that only emerge in very large datasets and to study multiple phenomena simultaneously, opening new perspectives on how the language system works as a whole. However, this abundance of data also creates methodological challenges: annotating and analyzing datasets of this scale quickly moves beyond the reach of human annotators. Artificial intelligence has therefore been explored as a solution. In this presentation, I illustrate the development of corpus linguistics towards big data and AI with examples from my research on Dutch and Swedish. I also take a step back to consider whether the move toward ever larger datasets is always necessary—or even desirable—in linguistic research.

Germanic languages

Evie Coussé is a linguist specializing in the Germanic languages. She obtained her PhD in Linguistics from Ghent University (Belgium) in 2008. Since 2010, she has been employed at the University of Gothenburg, where she became Associate Professor of Linguistics in 2014. Her research focuses on grammatical change in the Germanic languages—especially Dutch and Swedish—including word order change and the development of auxiliary verbs (grammaticalization). She studies these processes across a wide range of corpora, from medieval Bible translations to contemporary social media.

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