首页|Findings from Ghent University Broaden Understanding of Machine Learning (Keep m e PoS-ted: experimenting with Part-of-Speech prediction on Old Babylonian letter s)
Findings from Ghent University Broaden Understanding of Machine Learning (Keep m e PoS-ted: experimenting with Part-of-Speech prediction on Old Babylonian letter s)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting out of Ghent, Belgium, b y NewsRx editors, research stated, “Within this paper we will account for a coop eration between Ghent University based Assyriologists and computational linguist s that has set up a pilot study to analyse the language used in Old Babylonian ( OB) letters using Natural Language Processing (NLP) techniques.” Financial supporters for this research include Beslpo. The news correspondents obtained a quote from the research from Ghent University : “OB letters make up an interesting dataset because (1) they form an invaluable source for everyday vernacular language, and (2) more than 5000 have been recov ered, many of which are accessible in transliteration and translation through th e series Altbabylonische Briefe and the Cuneiform Digital Library Initiative. Ba sed on a first batch of letters from OB Sippar, later extended by other Akkadian letters, we aim to develop machine learning approaches to perform semi-automati c text analysis and annotation of the letters. We will here present a Part-of-Sp eech (PoS) tag prediction model using machine learning. The input data is Akkadi an in transliteration and the best performing model is a fine-tuned Multilingual BERT Transformer with Word embeddings (weighted avg F1: 90.19 %).”
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