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One of the primary tasks of morphological parsers is the disambiguation ofhomographs. Particularly difficult are cases of unbalanced ambiguity, where oneof 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 toproperly evaluate performance, nor to train effective classifiers. In thispaper 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. Weshow that the current SOTA of Hebrew disambiguation performs poorly on cases ofunbalanced ambiguity. Leveraging our new dataset, we achieve a newstate-of-the-art for all 21 words, improving the overall average F1 score from0.67 to 0.95. Our resulting annotated datasets are made publicly available forfurther research.

A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration