Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks
Bar-Ilan University（巴伊兰大学）；Davidson Institute of Science Education（戴维森科学教育研究所）；Weizmann Institute of Science（魏茨曼科学研究所）；Ariel University（阿里尔大学）
英文摘要：
The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Despite being an invaluable resource, many tablets are fragmented leading to missing information. Currently these missing parts are manually completed by experts. In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
