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.
|Title of host publication||Findings of the Association for Computational Linguistics Findings of ACL|
|Subtitle of host publication||EMNLP 2020|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||11|
|State||Published - 2020|
|Event||Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 - Virtual, Online|
Duration: 16 Nov 2020 → 20 Nov 2020
|Name||Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020|
|Conference||Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020|
|Period||16/11/20 → 20/11/20|
Bibliographical noteFunding Information:
The work of the last author has been supported by an ERC-StG grant #677352 and an ISF grant #1739/26. We acknowledge the substantial help of our programmers, Yehuda Broderick and Cheyn Shmuel Shmidman.
© 2020 Association for Computational Linguistics