Multilingual Named Entity Recognition in archaeology: an approach based on deep learning

Review of Archaeology specific BERT models for English, German, and Dutch

• omitting code sections and providing links to the scripts in footnotes;
• removing text closely tied to technical comments (e.g., lines 45-46 and 81).

Dear authors,

This paper offers a concise but intriguing paper look at an exciting area of recent advancement in archaeology. The use of a NER for extracting relevant concepts from abundant literature to find mention of artefacts, time periods, etc. has several useful implications. Can this be expanded on in the introduction?

What stands out in this paper, is that the authors provide a method by which others can contribute to the pre-training of the BERT models. This important aim and its potential broader implications should be mentioned in the introduction.

It would be useful to expand on the traditional use of CRF and rule-base methods to in turn, help explain why the BERT model is such an advance and to demonstrate how it is different. This would help to elevate the significance of using the BERT models particularly when GPT-3 is mentioned.

The innovative use of BERT models to contextualize the model before NLP tasks are carried out is a critical strength of this paper. The data are linked on HuggingFace, but it would be useful to see a visualization (or comparative visualization) of archaeological wordlists, akin to the one published in Delvin et al. 2019 (https://production-media.paperswithcode.com/methods/new_BERT_Overall.jpg) which will allow readers to immediately recognize the way word/concept selections are streamlined. 

The limitations of GPT-3 and ChatGPT relative to BERT could be expanded upon. Here it would be useful to see a part of the trial run on the Llama model, if possible, to again give readers a more specific idea of what the differences are between results achieved using the various approaches.

It would also be useful to see an example of how others might use NER in their own archaeological work.

Thank you.

How do we wring new insights from legacy archaeological data? And what new issues does that act of 'data wrangling' create? Hugget (2022) for instance explores in detail the 'many characters of data' and 'data imaginaries' that the fact of digital data capture, representation, and storage create. But nevertheless, a lot of these issues can feel remote when one is face to face with what sometimes passes for archaeological 'data': badly scanned and ocr'd pdfs of poorly laid out recording templates or desk reports. The high-level issues can sometimes feel quite remote. 

In which case, this welcome contribution by Alex Brandsen fills a gap in the middle between digitized data and the higher level issues. He takes existing computational language models for English, German, and Dutch and fine tunes these models on the particularities of archaeological reports, providing a tool for extracting data at scale from digitized data. 

In terms of the training, I might like to know a bit more about the process, in particular, the origin of the labelled NER data for the process. Also, more detail perhaps on how the training logic works might reassure the reader of the validity of the fine-tuning process. I know than Brandsen and team have written in more detail about the intricacies of this kind of work in other publications, but if we imagine the present article as a kind of paradata document for the creation and further elaboration of this process, some more nuts-and-bolts details might be welcomed by the reader. For instance, was the process to take an existing fill-mask BERT model (which one?) and then use a fill-mask approach on labelled archaeological texts - which in English was 44k documents? (I imagine it was a much smaller subset?) How long did it take to produce the annotated training data? Did Brandsen do that work himself, or with a team? How feasible is it for someone else to continue to improve these archaeological models - how much annotated data might one need to provide? I ask all this from the point of view of imagining writing a proposal to create a fine-tuned model on a more limited archaeological domain, having to think through labour requirements, computation requirements, budget requirements and so on. Brandsen's experience would be invaluable.

I found that I could not run the supplied code example in a Google Colab notebook (which gives me access to a GPU) until I set the runtime to gpu, and then used the pipeline as a high level helper (see code below) which is slightly different than what Brandsen reports:

```python
!pip install transformers
import transformers
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="alexbrandsen/ArchaeoBERT-NER")
predictor = pipeline(
 'ner',
 model=model,
 tokenizer=tokenizer,
 device = 0,
 grouped_entities = False
 )
sentence = "We have found a cup in a Medieval well."
entities = predictor(sentence)
```

which returns:
```json
[{'entity': 'B-ART',
  'score': 0.9598702,
  'index': 5,
  'word': 'cup',
  'start': 16,
  'end': 19},
 {'entity': 'B-PER',
  'score': 0.9939248,
  'index': 8,
  'word': 'Medieval',
  'start': 25,
  'end': 33},
 {'entity': 'B-CON',
  'score': 0.5886574,
  'index': 9,
  'word': 'well',
  'start': 34,
  'end': 38}]
```

I think it might be helpful for the reader if the actual output were presented in the piece and the meaning of the different keys and values was discussed. The coding literacy of archaeologists in general seems to be higher than other humanistic disciplines, perhaps, but it would be generous to the reader to expand a bit on the necessary setup and what the output looks like and how one might reshape this data into for instance a dataframe or csv file for further analysis - even just a small script to achieve that.

The piece raises the issue of Large Language Models being used for NER tasks; if anything here I think Brandsen undersells the value of the models he has created. The internal workings of a large language model are inscrutable, whereas Brandsen's models are at least intelligible and appropriate for the domain specific work one might want to do with them. The discussion might want to focus more on the positives of these models rather than framing them defensively in the light of LLMs. To my mind, Brandsen has performed an enormous service to the archaeological community by providing these models, and he might usefully expand on ways the models might be deployed. Further pre-training the models is gestured towards by linking to the HuggingFace documentation, but if Brandsen knows of or has available archaeological-specific documentation to achieve this it might be better to point the reader in that direction as well (in that some of the tacit things that organizations like HuggingFace assume their readers already know might not necessarily be known by the archaeologist interested in taking Brandsen's work further). 

I am glad Brandsen has done this work and made it available to the archaeological world; I feel these models will become foundational for data mining and reconsiderations of legacy archaeological materials and archives. 

Huggett, Jeremy. 2022. "Data Legacies, Epistemic Anxieties, and Digital Imaginaries in Archaeology" _Digital_ 2, no. 2: 267-295. https://doi.org/10.3390/digital2020016