首页|Studies from Al-Nahrain University Update Current Data on Machine Learning [Cuneiform Text Dialect Identification Using Machine Learning Algorithms and Natu ral Language Processing (NLP)]
Studies from Al-Nahrain University Update Current Data on Machine Learning [Cuneiform Text Dialect Identification Using Machine Learning Algorithms and Natu ral Language Processing (NLP)]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on artificial intelligence is now ava ilable. According to news originating from Al- Nahrain University by NewsRx edito rs, the research stated, “Due to a lack of resources and the tokenization issue, it is challenging to identify the languages inscribed in cuneiform symbols.” The news correspondents obtained a quote from the research from Al-Nahrain Unive rsity: “Sumerian and six dialects of the Akkadian language-Old Babylonian, Middl e Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, a nd Neo-Assyrian-are among the seven languages and dialects written in cuneiform that need to be identified. This problem is addressed by the Cuneiform Language Identification task in VarDial 2019. This paper presents ten machine learning al gorithms derived from four types of machine learning that were used (supervised, ensemble, instance-based, and Artificial Neural Network) learnings. The Support Vector Machine (SVM), Na Bayes (NB), Logistic Regression (LR), and Decision Tre e (DT) algorithms within supervised learning, the K-Nearest Neighbors algorithm (KNN) within instance- based learning, the Random Forest (RF), Adaptive Boosting (Adaboost), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) alg orithms within ensemble learning. Also, one of the natural language processing a lgorithms, n-gram, is used to identify the cuneiform dialect.”
Al-Nahrain UniversityAlgorithmsCybor gsEmerging TechnologiesMachine LearningNatural Language Processing
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