Update README.md

README.md

@@ -62,31 +62,27 @@ configs:
     
 ---
 
-# Danish medical word embeddings
+# Danish medical word embedding evaluation
 
-MeDa-We was trained on a Danish medical corpus of 123M tokens. The word embeddings are 300-dimensional and are trained using [FastText](https://fasttext.cc/).
-
-The embeddings were trained for 10 epochs using a window size of 5 and 10 negative samples.
-
-The development of the corpus and word embeddings is described further in our [paper](https://aclanthology.org/2023.nodalida-1.31/). 
-
-We also trained a transformer model on the developed corpus which can be found [here](https://huggingface.co/jannikskytt/MeDa-Bert).
+The development of the datasets is described further in our [paper](https://aclanthology.org/2023.nejlt-1.4/). 
 
 ### Citing
 
 ```
-@inproceedings{pedersen-etal-2023-meda,
-    title = "{M}e{D}a-{BERT}: A medical {D}anish pretrained transformer model",
-    author = "Pedersen, Jannik  and
-      Laursen, Martin  and
-      Vinholt, Pernille  and
+@inproceedings{laursen-etal-2023-benchmark,
+    title = "Benchmark for Evaluation of {D}anish Clinical Word Embeddings",
+    author = "Laursen, Martin Sundahl  and
+      Pedersen, Jannik Skyttegaard  and
+      Vinholt, Pernille Just  and
+      Hansen, Rasmus S{\o}gaard  and
       Savarimuthu, Thiusius Rajeeth",
-    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
-    month = may,
+    editor = "Derczynski, Leon",
+    booktitle = "Northern European Journal of Language Technology, Volume 9",
     year = "2023",
-    address = "T{\'o}rshavn, Faroe Islands",
-    publisher = "University of Tartu Library",
-    url = "https://aclanthology.org/2023.nodalida-1.31",
-    pages = "301--307",
+    address = {Link{\"o}ping, Sweden},
+    publisher = {Link{\"o}ping University Electronic Press},
+    url = "https://aclanthology.org/2023.nejlt-1.4",
+    doi = "https://doi.org/10.3384/nejlt.2000-1533.2023.4132",
+    abstract = "In natural language processing, benchmarks are used to track progress and identify useful models. Currently, no benchmark for Danish clinical word embeddings exists. This paper describes the development of a Danish benchmark for clinical word embeddings. The clinical benchmark consists of ten datasets: eight intrinsic and two extrinsic. Moreover, we evaluate word embeddings trained on text from the clinical domain, general practitioner domain and general domain on the established benchmark. All the intrinsic tasks of the benchmark are publicly available.",
 }
 ```