The project aims to build a system using ML.NET to classify Swedish text into its correct historical period, from Viking runes to modern language.
The idea for the project originated from a university exam on Swedish language history and observing linguistic differences on a Nobel Prize diploma.
Language evolution is a natural process for living languages, illustrated by comparing Old English to modern English and old C# syntax to new pattern matching.
The Swedish language is divided into distinct historical periods, including Runic, Old Swedish, and Modern Swedish, each with unique alphabets, grammar, and vocabulary.
ML.NET is an open-source framework that allows .NET developers to build machine learning models without needing deep expertise in underlying algorithms.
Preparing the dataset is the most time-consuming step, requiring cleaning inconsistent formats, removing irrelevant characters, and standardizing text units for training.
The ML.NET Model Builder in Visual Studio provides a simple UI to select a scenario, load data, and train a model with a single button click.
While the model successfully classifies valid Swedish text, it incorrectly categorizes any garbage or non-Swedish input as Runic Swedish, highlighting a data quality issue.
The Q&A covers topics like using ML.NET versus Python, the importance of balanced training data, and the model's inability to extrapolate future language changes.
msg
Ismaning, Germany
26:28Nils Kasseckert
47:28Tanmay Bakshi
57:46Adrian Schmitt
1:00:14Mihailo Joksimovic
52:37Kateryna Hrytsaienko
56:55Ekaterina Sirazitdinova
38:24Håkan Silfvernagel
1:11:20Marco Zamana
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Jobs that call for the skills explored in this talk.
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Control Expert GmbH
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