Workshop Description

Linguistic annotation of natural language corpora is the backbone of supervised methods of statistical natural language processing. The Linguistic Annotation Workshop (LAW) is the annual workshop of the ACL Special Interest Group on Annotation (SIGANN), and it provides a forum for the presentation and discussion of innovative research on all aspects of linguistic annotation, including the creation and evaluation of annotation schemes, methods for automatic and manual annotation, use and evaluation of annotation software and frameworks, representation of linguistic data and annotations, semi-supervised “human in the loop” methods of annotation, crowd-sourcing approaches, and more. As in the past, the LAW will provide a forum for annotation researchers to work towards standardization, best practices, and interoperability of annotation information and software.

Special Theme: Breaking the Mold

This year's special theme is “Breaking the Mold”, promoting novel or less studied types of annotations and data. In addition to general papers, we encourage papers on less common annotations (for example metaphor, sense anaphora, gesture, humor); annotations in understudied settings or data types (new text types or novel collection methods); and annotation for languages other than English, especially beyond tagging/treebanking.

Anti-Harassment Policy

ACL Anti-Harassment Policy.

Author Responsibilities

Papers must be of original, previously-unpublished work. Papers must be anonymized to support double-blind reviewing. If the paper is available as a preprint, this must be indicated on the submission form but not in the paper itself. In addition, COLING’2020 will follow the same policy as ACL'2018 establishing an anonymity period (from submission to author notification) during which non-anonymous posting of preprints is not allowed. Also included in that policy are instructions to reviewers to not rate papers down for not citing recent preprints. Authors are asked to cite published versions of papers instead of preprint versions when possible.