FigSearch: a figure legend indexing and classification system

Liu, Fang; Jenssen, Tor-Kristian; Nygaard, Vegard; Sack, John; Hovig, Eivind

doi:10.1093/bioinformatics/bth316

Cited by 24 publications

(5 citation statements)

References 9 publications

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“…Finally, we describe related work on text classification for the biomedical domain. There has been interest from many research groups in developing text mining tools [40,41,42] for the biomedical domain. Cohen and Hersch [43] provide a survey of work on this area.…”

Section: Text Classification For the Biomedical Domainmentioning

confidence: 99%

Improving MeSH classification of biomedical articles using citation contexts

Aljaber

Martínez

Stokes

et al. 2011

Journal of Biomedical Informatics

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Medical Subject Headings (MeSH) are used to index the majority of databases generated by the National Library of Medicine. Essentially, MeSH terms are designed to make information, such as scientific articles, more retrievable and assessable to users of systems such as PubMed. This paper proposes a novel method for automating the assignment of biomedical publications with MeSH terms that takes advantage of citation references to these publications. Our findings show that analysing the citation references that point to a document can provide a useful source of terms that are not present in the document. The use of these citation contexts, as they are known, can thus help to provide a richer document feature representation, which in turn can help improve text mining and information retrieval applications, in our case MeSH term classification. In this paper, we also explore new methods of selecting and utilising citation contexts. In particular, we assess the effect of weighting the importance of citation terms (found in the citation contexts) according to two aspects: (i) the section of the paper they appear in and (ii) their distance to the citation marker. We conduct intrinsic and extrinsic evaluations of citation term quality. For the intrinsic evaluation, we rely on the UMLS Metathesaurus conceptual database to explore the semantic characteristics of the mined citation terms. We also analyse the "informativeness" of these terms using a class-entropy measure. For the extrinsic evaluation, we run a series of automatic document classification experiments over MeSH terms. Our experimental evaluation shows that citation contexts contain terms that are related to the original document, and that the integration of this knowledge results in better classification performance compared to two state-of-the-art MeSH classification systems: MeSHUP and MTI. Our experiments also demonstrate that the consideration of Section and Distance factors can lead to statistically significant improvements in citation feature quality, thus opening the way for better document feature representation in other biomedical text processing applications.

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Section: Text Classification For the Biomedical Domainmentioning

confidence: 99%

Improving MeSH classification of biomedical articles using citation contexts

Aljaber

Martínez

Stokes

et al. 2011

Journal of Biomedical Informatics

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“…They later developed methods to extract facts from the captions referring to these images (Cohen et al, 2003). Liu et al (2004) collected a set of figures and classified them according to whether or not they depicted schematic representations of protein interactions. They then allowed users to search for a gene name within the figure caption, returning only those figures that fit within the one class (protein interaction schematics) and contained the gene name.…”

Section: Automated Caption Analysismentioning

confidence: 99%

Exploring the efficacy of caption search for bioscience journal search interfaces

Hearst

Divoli

et al. 2007

Proceedings of the Workshop on BioNLP 2007 Biological, Translational, and Clinical Language Processing - BioNLP '07

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This paper presents the results of a pilot usability study of a novel approach to search user interfaces for bioscience journal articles. The main idea is to support search over figure captions explicitly, and show the corresponding figures directly within the search results. Participants in a pilot study expressed surprise at the idea, noting that they had never thought of search in this way. They also reported strong positive reactions to the idea: 7 out of 8 said they would use a search system with this kind of feature, suggesting that this is a promising idea for journal article search.

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“…So far, all of these have used some subset of articles from the life sciences. FigSearch [7], developed in 2004, claims to be the first of these applications. The Yale Image Finder [9] was developed in 2008 Another search engine is Figuresearch [1] from 2009.Viziometrics [6] from 2016 is the newest application that allows users to directly search for images.…”

Section: Related Workmentioning

confidence: 99%