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README.md

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+---
+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)