Can BERT Dig It? -- Named Entity Recognition for Information Retrieval in the Archaeology Domain

The amount of archaeological literature is growing rapidly. Until recently, these data were only accessible through metadata search. We implemented a text retrieval engine for a large archaeological text collection ($\sim 658$ Million words). In archaeological IR, domain-specific entities such as locations, time periods, and artefacts, play a central role. This motivated the development of a named entity recognition (NER) model to annotate the full collection with archaeological named entities. In this paper, we present ArcheoBERTje, a BERT model pre-trained on Dutch archaeological texts. We compare the model's quality and output on a Named Entity Recognition task to a generic multilingual model and a generic Dutch model. We also investigate ensemble methods for combining multiple BERT models, and combining the best BERT model with a domain thesaurus using Conditional Random Fields (CRF). We find that ArcheoBERTje outperforms both the multilingual and Dutch model significantly with a smaller standard deviation between runs, reaching an average F1 score of 0.735. The model also outperforms ensemble methods combining the three models. Combining ArcheoBERTje predictions and explicit domain knowledge from the thesaurus did not increase the F1 score. We quantitatively and qualitatively analyse the differences between the vocabulary and output of the BERT models on the full collection and provide some valuable insights in the effect of fine-tuning for specific domains. Our results indicate that for a highly specific text domain such as archaeology, further pre-training on domain-specific data increases the model's quality on NER by a much larger margin than shown for other domains in the literature, and that domain-specific pre-training makes the addition of domain knowledge from a thesaurus unnecessary.

Usability Evaluation for Online Professional Search in the Dutch Archaeology Domain

This paper presents AGNES, the first information retrieval system for archaeological grey literature, allowing full-text search of these long archaeological documents. This search system has a web interface that allows archaeology professionals and scholars to search through a collection of over 60,000 Dutch excavation reports, totalling 361 million words. We conducted a user study for the evaluation of AGNES's search interface, with a small but diverse user group. The evaluation was done by screen capturing and a think aloud protocol, combined with a user interface feedback questionnaire. The evaluation covered both controlled use (completion of a pre-defined task) as well as free use (completion of a freely chosen task). The free use allows us to study the information needs of archaeologists, as well as their interactions with the search system. We conclude that: (1) the information needs of archaeologists are typically recall-oriented, often requiring a list of items as answer; (2) the users prefer the use of free-text queries over metadata filters, confirming the value of a free-text search system; (3) the compilation of a diverse user group contributed to the collection of diverse issues as feedback for improving the system. We are currently refining AGNES's user interface and improving its precision for archaeological entities, so that AGNES will help archaeologists to answer their research questions more effectively and efficiently, leading to a more coherent narrative of the past.