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• W2043928274 abstract "We present the application of probabilistic finite state transducers to the task of bibliographic meta-data extraction from scientific references. By using the transducer approach, which is often applied successfully in computational linguistics, we obtain a trainable and modular framework. This results in simplicity, flexibility, and easy adaptability to changing requirements. An evaluation on the Cora dataset that serves as a common benchmark for accuracy measurements yields a word accuracy of 88.5%, afield accuracy of 82.6%, and an instance accuracy of 42.7%. Based on a comparison to other published results, we conclude that our system performs second best on the given data set using a conceptually simple approach and implementation." @default.
• W2043928274 created "2016-06-24" @default.
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• W2043928274 date "2007-09-01" @default.
• W2043928274 modified "2023-09-25" @default.
• W2043928274 title "Bibliographic Meta-Data Extraction Using Probabilistic Finite State Transducers" @default.
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• W2043928274 doi "https://doi.org/10.1109/icdar.2007.4376987" @default.
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