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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.
This paper proposes a segment combination strategy for automatically classifying research methods in academic papers by partitioning full-text content. Experiments on an annotated corpus from Library and Information Science journals show that methodological information is unevenly distributed, with middle-to-late segments having higher discriminative power.