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README.md

@@ -1,78 +1,97 @@
+
 ---
-license: mit
-tags:
-- coreference-resolution
-language:
+language: 
 - ind
+pretty_name: Indocoref
+task_categories: 
+- coreference-resolution
+tags: 
+- coreference-resolution
 ---
 
-# 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`.
+## Languages
 
-## Citation
+ind
 
+## Supported Tasks
+
+Coreference Resolution
+    
+## Dataset Usage
+### Using `datasets` library
 ```
-@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.}
-}
+    from datasets import load_dataset
+    dset = datasets.load_dataset("SEACrowd/indocoref", trust_remote_code=True)
+```
+### Using `seacrowd` library
+```import seacrowd as sc
+# Load the dataset using the default config
+    dset = sc.load_dataset("indocoref", schema="seacrowd")
+# Check all available subsets (config names) of the dataset
+    print(sc.available_config_names("indocoref"))
+# Load the dataset using a specific config
+    dset = sc.load_dataset_by_config_name(config_name="<config_name>")
 ```
+    
+    More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
+    
+
+## Dataset Homepage
+
+[https://github.com/valentinakania/indocoref/](https://github.com/valentinakania/indocoref/)
+
+## Dataset Version
+
+Source: 1.0.0. SEACrowd: 2024.06.20.
 
-## License
+## Dataset License
 
 MIT
 
-## Homepage
+## Citation
 
-[https://github.com/valentinakania/indocoref/](https://github.com/valentinakania/indocoref/)
+If you are using the **Indocoref** dataloader in your work, please cite the following:
+```
+@inproceedings{artari-etal-2021-multi,
+    title        = {{A Multi-Pass Sieve Coreference Resolution for Indonesian}},
+    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.}
+}
 
-### NusaCatalogue
 
-For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
+@article{lovenia2024seacrowd,
+    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
+    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
+    year={2024},
+    eprint={2406.10118},
+    journal={arXiv preprint arXiv: 2406.10118}
+}
+
+```