mlsanityData to 7 Oct 2026

Multi-Round Parsing-based Multiword Rules for Scientific OpenIE

arXiv 2108.02074 ↗First posted 4 Aug 2021cs.CLcited by 0

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Abstract (arXiv)

Information extraction (IE) in scientific literature has facilitated many down-stream tasks. OpenIE, which does not require any relation schema but identifies a relational phrase to describe the relationship between a subject and an object, is being a trending topic of IE in sciences. The subjects, objects, and relations are often multiword expressions, which brings challenges for methods to identify the boundaries of the expressions given very limited or even no training data. In this work, we present a set of rules for extracting structured information based on dependency parsing that can be applied to any scientific dataset requiring no expert's annotation. Results on novel datasets show the effectiveness of the proposed method. We discuss negative results as well.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 4 Aug 2021.

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References · 6

  1. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  2. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  3. 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
  4. 2015Yara Parser: A Fast and Accurate Dependency Parsercited by 110
  5. 2020IMoJIE: Iterative Memory-Based Joint Open Information Extractioncited by 34
  6. 2019Span Model for Open Information Extraction on Accurate Corpuscited by 33

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