Biblical Hebrew, the sacred language of the Tanakh, presents unique grammatical challenges even for seasoned scholars. Its ancient nature, limited corpus, and the complexities introduced by the Masoretic textual tradition demand meticulous attention. Understanding its intricate morphology, where a single word can convey a root, conjugation, person, gender, number, and even prepositions, requires deep linguistic insight. Syntactic structures, often characterized by a VSO (Verb-Subject-Object) order with significant flexibility for emphasis, further complicate analysis. The Masoretic system, overlaying consonantal text with vowel points (niqqud) and cantillation marks (teamim), provides crucial interpretive guidance but also layers of complexity regarding pronunciation and grammatical parsing that must be precisely understood. Traditional learning methods, relying heavily on rote memorization, extensive dictionary use, and manual parsing, are foundational but often labor-intensive and time-consuming, highlighting a significant area where advanced computational approaches can provide transformative assistance.
Artificial Intelligence (AI) and its subfield, Natural Language Processing (NLP), offer a powerful paradigm shift in how we approach the study of ancient languages like Biblical Hebrew. At its core, NLP leverages machine learning (ML) techniques, particularly deep learning and