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ABSTRACT

The Fifth Message Understanding Conference (MUC-5) involved the same tasks, domains and languages as the information extraction portion of the ARPA TIPSTER program. These tasks center on automatically filling object-oriented data structures, called templates, with information extracted from free text in news stories (for discussion of templates and objects, see "Template Design for Information Extraction" in this volume). For each task, a generic type of information that is specified for extraction corresponds to each of the slots in the templates. With text as input, the MUC-5 systems first detect whether the text contains relevant information. If available, the systems extract specific instances of the generic types from the text and output that information by filling the template slots with the appropriately formatted data representations. These slots are then scored by using an automatic scoring program with analyst-produced templates as the keys. Human analysts also prepared development set templates for each domain, which served as training models for system developers (for discussion of the data preparation effort, see "Corpora and Data Preparation" in this volume).