Abstract

Wouldn’t it be helpful if your text editor automatically suggested papers that are contextually relevant to your work? We concern ourselves with this task: we desire to recommend contextually relevant citations to the author of a paper. A number of rhetorical annotation schemes for academic articles have been developed over the years, and it has often been suggested that they could find application in Information Retrieval scenarios such as this one. We investigate the usefulness for this task of two sentence-based, functional, scientific discourse annotation schemes: CoreSC for biomedical science (e.g. Hypothesis, Method, Result, etc.) and Argumentative Zoning (AZ) for computational linguistics. We apply this both to the contents of articles and to anchor text, that is, the text surrounding a citation, in citing articles. This is an important source of data for building document representations. By annotating each sentence in every document with CoreSC and AZ and indexing them separately by sentence class, we aim to build a more useful vector-space representation of documents in our collection. Our results show consistent links between types of citing sentences and types of cited sentences, both in cited articles and in anchor text, which we argue can indeed be exploited to increase the relevance of recommendations.