Antarlekhaka

A web-deployable annotation tool for distributed multi-task natural language annotation.

Features

Sequential annotation method
Eight categories of admission tasks with unique interfaces
Comprehensive task coverage

This work has been accepted in 3rd Workshop on NLP Open Source Software at the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023).

You may access the paper at arXiv. More details here.

Installation Instructions

Clone repository
pip install -r requirements.txt
Copy settings.sample.py to settings.py and make appropriate changes.
Run application server python server.py

Note: Sample corpus and table data is included in data/corpus/sample and data/tables/sample respectively.

Supported Tasks

Task 1: Sentence Boundary
Task 2: Canonical Token Order (a.k.a. Anvaya)
Task 3: Token Classification (e.g. Named Entity Recognition)
Task 4: Token Graph (e.g. Dependency Graph, Action Graph)
Task 5: Token Connection (e.g. Co-reference Resolution)
Task 6: Sentence Classification
Task 7: Sentence Graph (e.g. Discourse Graph)
Task 8: Token Text Annotation (e.g. Lemmatization)

Cite

Download Paper: arXiv

@inproceedings{terdalkar2023antarlekhaka,
  title         = {{Antarlekhaka}: A Comprehensive Tool for Multi-task Natural Language Annotation},
  author        = {Terdalkar, Hrishikesh and Bhattacharya, Arnab},
  year          = {2023},
  eprint        = {2310.07826},
  url           = {https://arxiv.org/abs/2310.07826},
  publisher     = {Association for Computational Linguistics},
  archiveprefix = {arXiv},
  keywords      = {Annotation Tool, Sequential Annotation, Natural Language Processing},
  booktitle     = {Proceedings of the 3rd Workshop on NLP Open Source Software at the 2023 Conference on Empirical Methods in Natural Language Processing},
  primaryclass  = {cs.CL},
  numpages      = {8},
  location      = {Singapore},
  series        = {NLP-OSS @ EMNLP}
}

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