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.
|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|
|Number of pages||6|
|Publication status||Published - 1 Jun 2014|