Bibliographic Meta-Data Extraction Using Probabilistic Finite State Transducers

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.