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ANERcorp - CAMeL Lab Train/Test Splits


Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.

In 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.

Changes from the original dataset include the following:

1) We corrected minor tag spelling errors.

2) We converted middle periods (·) and bold periods (•) to regular periods (.).

3) We removed the blank unicode character (\u200F).

4) We added sentence boundaries after sequences of 1 or more periods.

5) We split the dataset sequentially. The sentences containing the first 5/6 of the words go to train and the rest go to test. The train split has 125,102 words and the test split has 25,008 words.




By downloading the ANERcorp files from HERE you agree to the terms of the license below.

// License for ANERcorp -- CAMeL Lab Train/Test Splits

The ANERcorp files are provided under an Attribution-ShareAlike 4.0
International (CC BY-SA 4.0) License.

If you use this corpus split, please cite (a) the original ANERcorp paper and (b) the splits as specified in the CAMeLTools paper:

(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. "Anersys: An Arabic named entity recognition system based on maximum entropy." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.

(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. "CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing." In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.


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