Abstract
BackgroundDespite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear.ResultsPaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, we present how we appraise PaperBrowser's navigational functionalities in a user-based study using a text highlighting task and evaluation criteria of Human-Computer Interaction. Our results show that PaperBrowser reduces the amount of interactions between two highlighting events and therefore improves navigational efficiency by about 58% compared to the navigational mechanism that was previously available to the curators. Moreover, PaperBrowser is shown to provide curators with enhanced navigational utility by over 74% irrespective of the different ways in which they highlight text in the article.ConclusionWe show that state-of-the-art performance in certain NLP tasks such as Named Entity Recognition and Anaphora Resolution can be combined with the navigational functionalities of PaperBrowser to support curation quite successfully.
Highlights
Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear
Our results show that PaperBrowser improves navigational efficiency by about 58% and provides curators with enhanced utility by over 74% compared to the navigational mechanism that was previously available to them
That NLP can be a useful aid for curation has long been assumed within the biomedical text mining community
Summary
Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear. MedMiner [2], Textpresso [3], iHop [4] and EBIMed [5] are characteristic examples. Such systems are primarily designed to perform information retrieval, i.e. to return documents relevant to a query within a large collection. This is typically accomplished without incorporating advanced Natural Language Processing (NLP) techniques such as those discussed in [6]. With the exception of the BioText Search Engine [7], most of these systems are not reported to have been developed by soliciting input from their intended users.
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