基于导航的引文推荐候选扩展和预训练语言模型
Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation
摘要：
科学文献的引文推荐系统，帮助作者找到应该被引用的论文，有可能加快发现和发现科学探索的新途径。我们把这个任务作为一个排名的问题，我们解决了两个阶段的方法：候选人生成，然后重新排名。在这个框架内，我们适应科学领域的一个证明组合的基础上“袋的话”检索，然后重新评分与BERT模型。我们的实验表明，领域自适应的影响，无论是在预训练领域内的数据和利用领域内的词汇。此外，我们引入了一种新的基于导航的文档扩展策略，以丰富我们的神经模型处理的候选文档。在来自不同科学学科的三个不同的集合上，我们在引文推荐任务中取得了最好的报告结果。
英文摘要：
Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by re-ranking. Within this framework, we adapt to the scientific domain a proven combination based on "bag of words" retrieval followed by re-scoring with a BERT model. We experimentally show the effects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we introduce a novel navigation-based document expansion strategy to enrich the candidate documents processed by our neural models. On three different collections from different scientific disciplines, we achieve the best-reported results in the citation recommendation task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
