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Semisupervised Wrapper Choice and Generation for Print-Oriented Documents
Jan. 2014 (vol. 26 no. 1)
pp. 208-220
Alberto Bartoli, University of Trieste, Trieste
Giorgio Davanzo, University of Trieste, Trieste
Eric Medvet, University of Trieste, Trieste
Enrico Sorio, University of Trieste, Trieste
Information extraction from printed documents is still a crucial problem in many interorganizational workflows. Solutions for other application domains, for example, the web, do not fit this peculiar scenario well, as printed documents do not carry any explicit structural or syntactical description. Moreover, printed documents usually lack any explicit indication about their source. We present a system, which we call PATO, for extracting predefined items from printed documents in a dynamic multisource scenario. PATO selects the source-specific wrapper required by each document, determines whether no suitable wrapper exists, and generates one when necessary. PATO assumes that the need for new source-specific wrappers is a part of normal system operation: new wrappers are generated online based on a few point-and-click operations performed by a human operator on a GUI. The role of operators is an integral part of the design and PATO may be configured to accommodate a broad range of automation levels. We show that PATO exhibits very good performance on a challenging data set composed of more than 600 printed documents drawn from three different application domains: invoices, datasheets of electronic components, and patents. We also perform an extensive analysis of the crucial tradeoff between accuracy and automation level.
Index Terms:
Information retrieval,Data mining,Humans,Automation,Accuracy,Graphical user interfaces,Patents,data entry,Document management,administrative data processing,business process automation,retrieval models,human-computer interaction
Citation:
Alberto Bartoli, Giorgio Davanzo, Eric Medvet, Enrico Sorio, "Semisupervised Wrapper Choice and Generation for Print-Oriented Documents," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 208-220, Jan. 2014, doi:10.1109/TKDE.2012.254
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