Finding Pragmatic Differences Between Disciplines
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Abstract (arXiv)
Scholarly documents have a great degree of variation, both in terms of content (semantics) and structure (pragmatics). Prior work in scholarly document understanding emphasizes semantics through document summarization and corpus topic modeling but tends to omit pragmatics such as document organization and flow. Using a corpus of scholarly documents across 19 disciplines and state-of-the-art language modeling techniques, we learn a fixed set of domain-agnostic descriptors for document sections and "retrofit" the corpus to these descriptors (also referred to as "normalization"). Then, we analyze the position and ordering of these descriptors across documents to understand the relationship between discipline and structure. We report within-discipline structural archetypes, variability, and between-discipline comparisons, supporting the hypothesis that scholarly communities, despite their size, diversity, and breadth, share similar avenues for expressing their work. Our findings lay the foundation for future work in assessing research quality, domain style transfer, and further pragmatic analysis.
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References · 5
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
- 2019SECTOR: A Neural Model for Coherent Topic Segmentation and Classificationcited by 48
- 2020Meta Fine-Tuning Neural Language Models for Multi-Domain Text Miningcited by 6
- 2016OCR++: A Robust Framework For Information Extraction from Scholarly Articlescited by 5
- 2020Structure-Tags Improve Text Classification for Scholarly Document Quality Predictioncited by 3
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