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- W3217006972 abstract "The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context. To balance the tradeoff between speed and accuracy of citation recommendation in the context of a large-scale paper database, a viable approach is to first prefetch a limited number of relevant documents using efficient ranking methods and then to perform a fine-grained reranking using more sophisticated models. In that vein, BM25 has been found to be a tough-to-beat approach to prefetching, which is why recent work has focused mainly on the reranking step. Even so, we explore prefetching with nearest neighbor search among text embeddings constructed by a hierarchical attention network. When coupled with a SciBERT reranker fine-tuned on local citation recommendation tasks, our hierarchical Attention encoder (HAtten) achieves high prefetch recall for a given number of candidates to be reranked. Consequently, our reranker requires fewer prefetch candidates to rerank, yet still achieves state-of-the-art performance on various local citation recommendation datasets such as ACL-200, FullTextPeerRead, RefSeer, and arXiv." @default.
- W3217006972 created "2021-12-06" @default.
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- W3217006972 date "2022-01-01" @default.
- W3217006972 modified "2023-10-03" @default.
- W3217006972 title "Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-Based Reranking" @default.
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- W3217006972 doi "https://doi.org/10.1007/978-3-030-99736-6_19" @default.
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