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Process-Level Representation of Scientific Protocols with Interactive Annotation

arXiv 2101.10244 ↗First posted 25 Jan 2021cs.CLcited by 16

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Abstract (arXiv)

We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments. We manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. We use this data to develop graph-prediction models, finding them to be good at entity identification and local relation extraction, while our corpus facilitates further exploration of challenging long-range relations.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 25 Jan 2021.

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17 of 37 references.

References · 17

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  3. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  4. 2018TextWorld: A Learning Environment for Text-based Gamescited by 304
  5. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  6. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 261
  7. 2016Discriminative Embeddings of Latent Variable Models for Structured Datacited by 130
  8. 2019A General Framework for Information Extraction using Dynamic Span Graphscited by 93
  9. 2018Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehensioncited by 62
  10. 2017Simulating Action Dynamics with Neural Process Networkscited by 53
  11. 2020Learning Dynamic Belief Graphs to Generalize on Text-Based Gamescited by 49
  12. 2019The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structurescited by 48
5 more
  1. 2020A Dataset for Tracking Entities in Open Domain Procedural Textcited by 37
  2. 2016An Incremental Parser for Abstract Meaning Representationcited by 34
  3. 2018An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocolscited by 18
  4. 2020Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literaturecited by 15
  5. 2018Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Textcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 5; 2024: 0; 2025: 0; 2026: 2.

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  1. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 298
  2. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Researchcited by 143
  3. 2023BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biologycited by 42
  4. 2021Pre-train or Annotate? Domain Adaptation with a Constrained Budgetcited by 27
  5. 2023Schema-Driven Information Extraction from Heterogeneous Tablescited by 24
  6. 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
  7. 2021Modeling Worlds in Textcited by 11
  8. 2022Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Expertscited by 10
  9. 2022PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Textcited by 6
  10. 2022SynKB: Semantic Search for Synthetic Procedurescited by 4
  11. 2021Situated Language Learning via Interactive Narrativescited by 2
  12. 2022Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decodingcited by 1
4 more
  1. 2023Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Gamescited by 1
  2. 2023Towards Flow Graph Prediction of Open-Domain Procedural Textscited by 0
  3. 2026Notes2Skills: From Lab Notebooks to Certainty-Aware Scientific Agent Skillscited by 0
  4. 2026Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planningcited by 0