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This paper constructs large-scale algorithm co-occurrence networks from the full text of academic papers to study the collective influence of algorithms in NLP, finding that classic, high-performing, and intersectional algorithms hold central network positions.
本文提出了一种基于全文内容分段的组合策略，用于自动分类学术论文中的研究方法。在来自图书馆与信息科学期刊的标注语料库上的实验表明，方法信息分布不均匀，中后段具有更高的区分能力。