pith. sign in

arxiv: 2101.10281 · v1 · pith:MGG3E3YE · submitted 2021-01-25 · cs.CL

PAWLS: PDF Annotation With Labels and Structure

pith:MGG3E3YEopen to challenge →

classification cs.CL
keywords pawlsannotationdocumentsallenaiavailabledocumentformathttps
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.