Abstract / Description of output
Wouldn't it be helpful if your text editor automatically suggested papers that are relevant to your research? Wouldn't it be even better if those suggestions were contextually relevant? In this paper we name a system that would accomplish this a context-based citation recommendation (CBCR) system. We specifically present Citation Resolution, a method for the evaluation of CBCR systems which exclusively uses readily-available scientific articles. Exploiting the human judgements that are already implicit in available resources, we avoid purpose-specific annotation. We apply this evaluation to three sets of methods for representing a document, based on a) the contents of the document, b) the surrounding contexts of citations to the document found in other documents, and c) a mixture of the two.
| Original language | English |
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| Title of host publication | Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) |
| Place of Publication | Baltimore, Maryland |
| Publisher | Association for Computational Linguistics |
| Pages | 358-363 |
| Number of pages | 6 |
| ISBN (Print) | 978-1-937284-73-2 |
| Publication status | Published - 1 Jun 2014 |