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The amount of archaeological literature is growing rapidly. Until recently,these data were only accessible through metadata search. We implemented a textretrieval engine for a large archaeological text collection ($\sim 658$ Millionwords). In archaeological IR, domain-specific entities such as locations, timeperiods, and artefacts, play a central role. This motivated the development ofa named entity recognition (NER) model to annotate the full collection witharchaeological named entities. In this paper, we present ArcheoBERTje, a BERTmodel pre-trained on Dutch archaeological texts. We compare the model's qualityand output on a Named Entity Recognition task to a generic multilingual modeland a generic Dutch model. We also investigate ensemble methods for combiningmultiple BERT models, and combining the best BERT model with a domain thesaurususing Conditional Random Fields (CRF). We find that ArcheoBERTje outperformsboth the multilingual and Dutch model significantly with a smaller standarddeviation between runs, reaching an average F1 score of 0.735. The model alsooutperforms ensemble methods combining the three models. Combining ArcheoBERTjepredictions and explicit domain knowledge from the thesaurus did not increasethe F1 score. We quantitatively and qualitatively analyse the differencesbetween the vocabulary and output of the BERT models on the full collection andprovide some valuable insights in the effect of fine-tuning for specificdomains. Our results indicate that for a highly specific text domain such asarchaeology, further pre-training on domain-specific data increases the model'squality on NER by a much larger margin than shown for other domains in theliterature, and that domain-specific pre-training makes the addition of domainknowledge from a thesaurus unnecessary.

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