Citation Recommendation based on Argumentative Zoning of User Queries
This work addresses a domain-specific need for scholars by enhancing citation recommendation through argumentative analysis, but it is incremental as it builds on existing methods.
The paper tackled the problem of citation recommendation by incorporating argumentative zoning to capture authors' citing intents, resulting in improved performance as shown in experiments.
Citation recommendation aims to locate the important papers for scholars to cite. When writing the citing sentences, the authors usually hold different citing intents, which are referred to citation function in citation analysis. Since argumentative zoning is to identify the argumentative and rhetorical structure in scientific literature, we want to use this information to improve the citation recommendation task. In this paper, a multi-task learning model is built for citation recommendation and argumentative zoning classification. We also generated an annotated corpus of the data from PubMed Central based on a new argumentative zoning schema. The experimental results show that, by considering the argumentative information in the citing sentence, citation recommendation model will get better performance.