|
--- |
|
license: mit |
|
tags: |
|
- coreference-resolution |
|
language: |
|
- ind |
|
--- |
|
|
|
# indocoref |
|
|
|
Dataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions: |
|
|
|
- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for films, |
|
|
|
TV show episodes, and novel stories; (ii) biographies (incl. fictional characters); and (iii) historical events or important events. |
|
|
|
- The pages contain significant variation of pronoun and named-entity. We count the number of first, second, third person pronouns, |
|
|
|
and clitic pronouns in the document by applying string matching.We examine the number |
|
|
|
of named-entity using the Stanford CoreNLP |
|
|
|
NER Tagger (Manning et al., 2014) with a |
|
|
|
model trained from the Indonesian corpus |
|
|
|
taken from Alfina et al. (2016). |
|
|
|
The Wikipedia texts have length of 500 to |
|
|
|
2000 words. |
|
|
|
We sample 201 of pages from subset of filtered |
|
|
|
Wikipedia pages. We hire five annotators who are |
|
|
|
undergraduate student in Linguistics department. |
|
|
|
They are native in Indonesian. Annotation is carried out using the Script d’Annotation des Chanes |
|
|
|
de Rfrence (SACR), a web-based Coreference resolution annotation tool developed by Oberle (2018). |
|
|
|
From the 201 texts, there are 16,460 mentions |
|
|
|
tagged by the annotators |
|
|
|
## Dataset Usage |
|
|
|
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. |
|
|
|
## Citation |
|
|
|
``` |
|
@inproceedings{artari-etal-2021-multi, |
|
title = {A Multi-Pass Sieve Coreference Resolution for {I}ndonesian}, |
|
author = {Artari, Valentina Kania Prameswara and Mahendra, Rahmad and Jiwanggi, Meganingrum Arista and Anggraito, Adityo and Budi, Indra}, |
|
year = 2021, |
|
month = sep, |
|
booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)}, |
|
publisher = {INCOMA Ltd.}, |
|
address = {Held Online}, |
|
pages = {79--85}, |
|
url = {https://aclanthology.org/2021.ranlp-1.10}, |
|
abstract = {Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74{\%} of MUC F-measure and 52.18{\%} of BCUBED F-measure.} |
|
} |
|
``` |
|
|
|
## License |
|
|
|
MIT |
|
|
|
## Homepage |
|
|
|
[https://github.com/valentinakania/indocoref/](https://github.com/valentinakania/indocoref/) |
|
|
|
### NusaCatalogue |
|
|
|
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |