mlsanityData to 14 Oct 2026

PAWLS: PDF Annotation With Labels and Structure

arXiv 2101.10281 ↗First posted 25 Jan 2021cs.CLcited by 4

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Abstract (arXiv)

Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult. In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format. PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models. A read-only PAWLS server is available at https://pawls.apps.allenai.org/ and the source code is available at https://github.com/allenai/pawls.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

14 of 27 references.

References · 14

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2 more
  1. 2017ConStance: Modeling Annotation Contexts to Improve Stance Classificationcited by 7
  2. 2019SLATE: A Super-Lightweight Annotation Tool for Expertscited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 1.

4 papers.