Citation Recommendation based on Argumentative Zoning of User Queries
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913671166296064 |
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| author | Ma, Shutian Zhang, Chengzhi Zhang, Heng Gao, Zheng |
| author_facet | Ma, Shutian Zhang, Chengzhi Zhang, Heng Gao, Zheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Citation Recommendation based on Argumentative Zoning of User Queries Ma, Shutian Zhang, Chengzhi Zhang, Heng Gao, Zheng Information Retrieval Computation and Language Digital Libraries 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. |
| title | Citation Recommendation based on Argumentative Zoning of User Queries |
| topic | Information Retrieval Computation and Language Digital Libraries |
| url | https://arxiv.org/abs/2501.18292 |