Computational Linguistics For Metadata Building (climb): Using Text Mining For The Automatic Identification, Categorization, And Disambiguation Of Subject Terms For Image Metadata

摘要

In this paper, we present a system using computational linguistic techniques to extract metadata for image access. We discuss the implementation, functionality and evaluation of an image catalogers' toolkit, developed in the Computational Linguistics for Metadata Building (CLiMB) research project. We have tested components of the system, including phrase finding for the art and architecture domain, functional semantic labeling using machine learning, and disambiguation of terms in domain-specific text vis a vis a rich thesaurus of subject terms, geographic and artist names. We present specific results on disambiguation techniques and on the nature of the ambiguity problem given the thesaurus, resources, and domain-specific text resource, with a comparison of domain-general resources and text. Our primary user group for evaluation has been the cataloger expert with specific expertise in the fields of painting, sculpture, and vernacular and landscape architecture.