The growing availability of computer-readable language data, increasing computational power and rapidly evolving statistical methodologies have had a profound effect on how scholars study and analyse human language use. However, the fields of linguistics, computer science and digital humanities have largely developed their own separate approaches and paradigms, often failing to communicate across disciplines in an effective way.

Language, Data Science and Digital Humanities bridges these disciplinary gaps by publishing monographs and edited volumes that explore disciplinary synergies and introduce new theoretical principles. Written in clear and transparent language, these books offer cutting-edge digital methodologies and create new opportunities for understanding how problems and research questions can be approached from different perspectives.

The methodological range of the series covers empirical linguistics, natural language processing, machine learning, data visualization, text mining, mark-up and annotation, statistical tools in analysing language data, and multimodal analysis. The volumes explain methodological solutions in detail using worked examples, and are supported by companion websites, allowing authors to share primary data, scripts, sophisticated data visualizations and other digital content.