LaTeCH-CLfL 2018

In the Humanities, Social Sciences, and Cultural Heritage communities, there is increasing interest in and demand for NLP methods for semantic annotation, intelligent linking, discovery, querying, cleaning and visualization of both primary and secondary data; this is even true of primarily non-textual collections, given that text is also the pervasive medium for metadata. Such applications pose new challenges for NLP research, such as noisy, non-standard textual or multi-modal input, historical languages, vague research concepts, multilingual parts within one document, lack of digital resources, or resource-intensive approaches that call for (semi-)automatic processing tools and domain adaptation, or, as a last resort, intense manual effort (e.g., annotation). Literary texts bring their own problems, because navigating this form of creative expression requires more than the typical information-seeking tools. Examples of advanced tasks include the study of literature of a certain period or sub-genre, recognition of certain literary devices, or quantitative analysis of poetry. More generally, there is a growing interest in computational models whose results can be interpreted in meaningful ways.  For these reasons, it is of mutual benefit that NLP experts, data specialists, and Digital Humanities researchers working in and across these domains get involved in the Computational Linguistics community and present their fundamental or applied research results. By this cross-disciplinary exchange, we envisage not only to support work in the Humanities, Social Sciences, and Cultural Heritage communities but also to promote work in the Computational Linguistics community to build richer and more effective tools and models.

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