metadata

language:
  - nl
license: other
task_categories:
  - token-classification
pretty_name: Dutch Archaeology NER Dataset
license_name: hippocratic-license-3.0
license_link: https://firstdonoharm.dev/version/3/0/full.md
dataset_info:
  features:
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-ART
            '2': I-ART
            '3': B-CON
            '4': I-CON
            '5': B-LOC
            '6': I-LOC
            '7': B-MAT
            '8': I-MAT
            '9': B-PER
            '10': I-PER
            '11': B-SPE
            '12': I-SPE
  splits:
    - name: fold1_train
      num_bytes: 4490700
      num_examples: 22150
    - name: fold1_validation
      num_bytes: 1579488
      num_examples: 5852
    - name: fold1_test
      num_bytes: 1574291
      num_examples: 5750
    - name: fold2_train
      num_bytes: 4685070
      num_examples: 22465
    - name: fold2_validation
      num_bytes: 1379777
      num_examples: 5431
    - name: fold2_test
      num_bytes: 1579700
      num_examples: 5865
    - name: fold3_train
      num_bytes: 4762905
      num_examples: 19560
    - name: fold3_validation
      num_bytes: 1501653
      num_examples: 8757
    - name: fold3_test
      num_bytes: 1379769
      num_examples: 5427
    - name: fold4_train
      num_bytes: 4533412
      num_examples: 17029
    - name: fold4_validation
      num_bytes: 1609278
      num_examples: 7963
    - name: fold4_test
      num_bytes: 1501649
      num_examples: 8755
    - name: fold5_train
      num_bytes: 4460910
      num_examples: 20039
    - name: fold5_validation
      num_bytes: 1574155
      num_examples: 5747
    - name: fold5_test
      num_bytes: 1609342
      num_examples: 7965
  download_size: 7478347
  dataset_size: 38222099
configs:
  - config_name: default
    data_files:
      - split: fold1_train
        path: data/fold1_train-*
      - split: fold1_validation
        path: data/fold1_validation-*
      - split: fold1_test
        path: data/fold1_test-*
      - split: fold2_train
        path: data/fold2_train-*
      - split: fold2_validation
        path: data/fold2_validation-*
      - split: fold2_test
        path: data/fold2_test-*
      - split: fold3_train
        path: data/fold3_train-*
      - split: fold3_validation
        path: data/fold3_validation-*
      - split: fold3_test
        path: data/fold3_test-*
      - split: fold4_train
        path: data/fold4_train-*
      - split: fold4_validation
        path: data/fold4_validation-*
      - split: fold4_test
        path: data/fold4_test-*
      - split: fold5_train
        path: data/fold5_train-*
      - split: fold5_validation
        path: data/fold5_validation-*
      - split: fold5_test
        path: data/fold5_test-*
tags:
  - archaeology

Dutch Archaeology NER Dataset

A selection of Dutch archaeology field reports, annotated by archaeology students from Leiden University.

Labels

The following labels are included:

ART, artefacts ('bijl', 'pijlpunt')
MAT, materials ('vuursteen', 'ijzer')
PER, time periods ('Middeleeuwen', '400 v. Chr.')
CON, archaeological contexts ('greppel','beerput')
LOC, locations ('Amsterdam', 'Oss')
SPE, species ('Betula nana', 'koe')

Folds

The reason I supply 5 folds is because I get wildly different F1 scores between folds, and because it's important to keep whole documents in folds: these are long documents, any document that's split between train and test instantly leads to a higher F1, as the model starts recognising specific tokens as entities, leading to overfitting. A micro average F1 over 5 folds with no split documents seems like the fairest evaluation, closest to real-world inference.

Citation Information

@inproceedings{brandsen-etal-2020-creating,
    title = "Creating a Dataset for Named Entity Recognition in the Archaeology Domain",
    author = "Brandsen, Alex  and
      Verberne, Suzan  and
      Wansleeben, Milco  and
      Lambers, Karsten",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.562",
    pages = "4573--4577",
    abstract = "In this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain. This dataset was created as there is a dire need for semantic search within archaeology, in order to allow archaeologists to find structured information in collections of Dutch excavation reports, currently totalling around 60,000 (658 million words) and growing rapidly. To guide this search task, NER is needed. We created rigorous annotation guidelines in an iterative process, then instructed five archaeology students to annotate a number of documents. The resulting dataset contains {\textasciitilde}31k annotations between six entity types (artefact, time period, place, context, species {\&} material). The inter-annotator agreement is 0.95, and when we used this data for machine learning, we observed an increase in F1 score from 0.51 to 0.70 in comparison to a machine learning model trained on a dataset created in prior work. This indicates that the data is of high quality, and can confidently be used to train NER classifiers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}