KennethEnevoldsen/danish-compounds

Danish Compounds

Danish Compounds consists of a list of Danish compound words annotated for constituent words and interfixes. This can be used to evaluate how close a model can obtain similar splits.

Loading the dataset

To load the dataset simply run:

from datasets import load_dataset

ds = load_dataset("KennethEnevoldsen/danish-compounds", split="train")

Sample

A sample from the dataset looks as follows:

{
    "words": "A-aktie",
    "annotated_splits": "A|-|aktie",
    "splits": [["A"], ["-"], ["aktie"]],
    "source": "wiktionary",
}

Where:

• words: The word as a string
• annotated_splits: The annotated splits, including primary and secondary splits
• splits: The words.
• source Where the word was found.

Dataset Source

The dataset is derived from two source Wiktionary and sproget.dk.

Annotation Creator

The word splits were manually annotated by Kenneth Enevoldsen.

Annotation Scheme

Words that the annotator did not understand (e.g. some Icelandic words) were removed.

• "|" indicates primary split (into compound words)
• "&" indicates secondary split (used in case of compound word existing of a multiple compounds) e.g. blindtarmsbetændelse consist of two compounds "blindtarm" "betændelse" with a 'lim-s' ("s"), but blindtarm is itself a compound, namely "blind", "tarm".

Reason for annotation

This dataset was initially annotated to get a gist of how well BPE, Unigram, and similar tokenizers obtain tokens similar to conventional splits.

The Jaccard index, also sometimes called the Jaccard similarity, can be seen as a similarity metric between the labeled splits and the splits of the model.

It was annotated as a part of Kenneth Enevoldsen's master thesis.

Citation

If you use this work please cite:

@mastersthesis{Enevoldsen2022,
  title     = {Representation Learning for Decision-Support in Clinical Psychiatry},
  author    = {Kenneth Enevoldsen},
  year      = {2022},
  school    = {Aarhus University},
  note      = {Master's Thesis}
}