Digging in documents: using text mining to access the hidden knowledge in Dutch archaeological excavation reports

Brandsen, A. (2022)

Doctoral Thesis

The archaeology domain produces large amounts of texts, too much to effectively read or manually search through for research. To alleviate this problem, we created a search system (called AGNES), which combines full text search with entity and geographical search. We first created a manually labelled data set to train a Named Entity Recognition model, which is used to extract entities from text. We also did a user requirement study, and usability evaluation on the system, to make sure it is suitable for archaeological research. In a case study on Early Medieval cremations, we show that using AGNES leads to a knowledge increase when compared to the knowledge of experts, gathered using previously available search engines. This shows that this kind of intelligent search system can help with literature research, find more relevant data, and lead to a better understanding of the past.

• Digital Archaeology
• Text Mining
• Information Retrieval
• Grey Literature
• Computational Archaeology
• Machine Learning
• Deep Learning

• All authors: Brandsen, A.
• Supervisor: Lambers, K.; Kolen, J.C.A.
• Co-supervisor: Verberne, S.
• Committee: Bosch, A.P.J. van den; Brysbaert, A.N.; Erp, M.G.J. van; Fontijn, D.R.; Kraaij, W.; Richards, J.D.; Weerdt, H.G.D.G. de
• Qualification: Doctor (dr.)
• Awarding Institution: Faculty of Archaeology, Leiden University
• Date: 2022-02-15