English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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. In this paper we investigate the usefulness for this task of CoreSC, a sentence-based, functional, scientific discourse annotation scheme (e.g. Hypothesis, Method, Result, etc.). We specifically apply this to anchor text, that is, the text surrounding a citation, which is an important source of data for building document representations. By annotating each sentence in every document with CoreSC 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 in anchor text, which we argue can indeed be exploited to increase the relevance of recommendations.

Rhetorical Classification of Anchor Text for Citation Recommendation