Joshua Guedalia

4 paper cards in Mova's library · 2020–2025

Joshua Guedalia wrote 4 of the papers Mova reads, in 2020–2025, most of them on Part-of-speech tagging; often with Avi Shmidman, Moshe Koppel, Shaltiel Shmidman.

The profile is the library's match of the name; two people with one name are not always told apart.

Papers

Automatic Text Segmentation of Ancient and Historic Hebrew (2025)
Unpunctuated Rabbinic and historic Hebrew and Aramaic texts run to hundreds of words without a full stop, and most neural tools need input cut into chunks.
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language (2022)
A short paper releasing a BERT-base model trained on about 220 million words of Rabbinic Hebrew (the Sefaria and Dicta libraries).
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All (2022)
The authors train AlephBERTGimmel, a Modern Hebrew BERT with a 128K word-piece vocabulary (earlier models had 2.4K–52K), on the same ~2 billion words as AlephBERT.
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration (2020)
Hebrew homographs are often unbalanced: one analysis is 30 to 129 times more frequent than the other. A tagger can then score well on the majority reading and fail on the minority one, which is the reading that matters in rare cases. The authors build a challenge set of 21 homographs (12 unbalanced, 9 balanced) with…