Using Non-Lexical Features to Identify Effective Indexing Terms for Biomedical Illustrations

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 737–744, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Using Non-lexical Features to Identify Effective Indexing Terms for Biomedical Illustrations Matthew Simpson, Dina Demner-Fushman, Charles Sneiderman, Sameer K. Antani, George R. Thoma Lister Hill National Center for Biomedical Communications National Library of Medicine, NIH, Bethesda, MD, USA {simpsonmatt, ddemner, csneiderman, santani, gthoma}@mail.nih.gov Abstract Automatic image annotation is an attrac- tive approach for enabling convenient ac- cess to images found in a variety of docu- ments. Since image captions and relevant discussions found in the text can be useful for summarizing the content of images, it is also possible that this text can be used to generate salient indexing terms. Unfortu- nately, this problem is generally domain- specific because indexing terms that are useful in one domain can be ineffective in others. Thus, we present a supervised machine learning approach to image an- notation utilizing non-lexical features1 ex- tracted from image-related text to select useful terms. We apply this approach to several subdomains of the biomedical sci- ences and show that we are able to reduce the number of ineffective indexing terms. 1 Introduction Authors of biomedical publications often utilize images and other illustrations to convey informa- tion essential to the article and to support and re- inforce textual content. These images are useful in support of clinical decisions, in rich document summaries, and for instructional purposes. The task of delivering these images, and the publica- tions in which they are contained, to biomedical clinicians and researchers in an accessible way is an information retrieval problem. Current research in the biomedical domain (e.g., Antani et al., 2008; Florea et al., 2007), has in- vestigated hybrid approaches to image retrieval, combining elements of content-based image re- trieval (CBIR) and annotation-based image re- trieval (ABIR). ABIR, compared to the image- 1Non-lexical features describe attributes of image-related text but not the text itself, e.g., unlike a bag-of-words model. only approach of CBIR, offers a practical advan- tage in that queries can be more naturally specified by a human user (Inoue, 2004). However, manu- ally annotating biomedical images is a laborious and subjective task that often leads to noisy results. Automatic image annotation is a more robust approach to ABIR than manual annotation. Un- fortunately, automatically selecting the most ap- propriate indexing terms is an especially challeng- ing problem for biomedical images because of the domain-specific nature of these images and the many vocabularies used in the biomedical sci- ences. For example, the term “sweat gland adeno- carcinoma” could be a useful indexing term for an image found in a dermatology publication, but it is less likely to have much relevance in describing an image from a cardiology publication. On the other hand, the term “mitral annular calcification” may be of great relevance for cardiology images, but of little relevance for dermatology ones. Our problem may be summarized as follows: Given an image, its caption, its discussion …

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