  One of the primary tasks of morphological parsers is the disambiguation of
homographs. Particularly difficult are cases of unbalanced ambiguity, where one
of the possible analyses is far more frequent than the others. In such cases,
there may not exist sufficient examples of the minority analyses in order to
properly evaluate performance, nor to train effective classifiers. In this
paper we address the issue of unbalanced morphological ambiguities in Hebrew.
We offer a challenge set for Hebrew homographs -- the first of its kind --
containing substantial attestation of each analysis of 21 Hebrew homographs. We
show that the current SOTA of Hebrew disambiguation performs poorly on cases of
unbalanced ambiguity. Leveraging our new dataset, we achieve a new
state-of-the-art for all 21 words, improving the overall average F1 score from
0.67 to 0.95. Our resulting annotated datasets are made publicly available for
further research.
