  In this paper, we present a character-based BiLSTM model for splitting
Icelandic compound words, and show how varying amounts of training data affects
the performance of the model. Compounding is highly productive in Icelandic,
and new compounds are constantly being created. This results in a large number
of out-of-vocabulary (OOV) words, negatively impacting the performance of many
NLP tools. Our model is trained on a dataset of 2.9 million unique word forms
and their constituent structures from the Database of Icelandic Morphology. The
model learns how to split compound words into two parts and can be used to
derive the constituent structure of any word form. Knowing the constituent
structure of a word form makes it possible to generate the optimal split for a
given task, e.g., a full split for subword tokenization, or, in the case of
part-of-speech tagging, splitting an OOV word until the largest known
morphological head is found. The model outperforms other previously published
methods when evaluated on a corpus of manually split word forms. This method
has been integrated into Kvistur, an Icelandic compound word analyzer.
