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Mathematical Entity Linking Methods and Applications
Mathematical Entity Linking Methods and Applications
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This research book explores the adaptation of traditional Entity Linking techniques to Mathematical Entity Linking (MathEL) for STEM disciplines, addressing the limitations of current Information Retrieval methods in handling mathematical expressions. By developing and evaluating novel MathEL approaches using AI, Machine Learning, and the Wikidata Knowledge Graph, significant progress is achieved in areas such as Formula Concept recognition, semantic formula search, mathematical question answer…
  • Leidėjas:
  • ISBN-10: 3658464739
  • ISBN-13: 9783658464738
  • Formatas: 14.8 x 21 x 1.5 cm, minkšti viršeliai
  • Kalba: Anglų

Mathematical Entity Linking Methods and Applications (el. knyga) (skaityta knyga) | knygos.lt

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This research book explores the adaptation of traditional Entity Linking techniques to Mathematical Entity Linking (MathEL) for STEM disciplines, addressing the limitations of current Information Retrieval methods in handling mathematical expressions. By developing and evaluating novel MathEL approaches using AI, Machine Learning, and the Wikidata Knowledge Graph, significant progress is achieved in areas such as Formula Concept recognition, semantic formula search, mathematical question answering, physics exam question generation, and STEM document classification. The study also introduces a suite of open-source Wikimedia MathEL tools, including AnnoMathTeX, MathQA, and PhysWikiQuiz, designed to advance Mathematical Information Retrieval and support innovative applications in academic and educational contexts.

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  • Autorius: Philipp Scharpf
  • Leidėjas:
  • ISBN-10: 3658464739
  • ISBN-13: 9783658464738
  • Formatas: 14.8 x 21 x 1.5 cm, minkšti viršeliai
  • Kalba: Anglų

This research book explores the adaptation of traditional Entity Linking techniques to Mathematical Entity Linking (MathEL) for STEM disciplines, addressing the limitations of current Information Retrieval methods in handling mathematical expressions. By developing and evaluating novel MathEL approaches using AI, Machine Learning, and the Wikidata Knowledge Graph, significant progress is achieved in areas such as Formula Concept recognition, semantic formula search, mathematical question answering, physics exam question generation, and STEM document classification. The study also introduces a suite of open-source Wikimedia MathEL tools, including AnnoMathTeX, MathQA, and PhysWikiQuiz, designed to advance Mathematical Information Retrieval and support innovative applications in academic and educational contexts.

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