Update README.md

README.md

@@ -94,4 +94,60 @@ language:
 tags:
 - archaeology
 pretty_name: Dutch Archaeology NER Dataset
----
+---
+
+# 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",
+}
+
+```