ANETAC: Arabic Named Entity Transliteration and Classification Dataset
Published in
arXiv.org(2019)External Links:
TL
TL;DR
The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes.
Abstract
In this paper, we make freely accessible ANETAC, our English-Arabic named entity transliteration and classification dataset that we built from freely available parallel translation corpora. The dataset contains 79, 924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes. This dataset was developed and used as part of a previous research study done by Hadj Ameur et al. [1].
References8 items
1
Sequence-to-sequence neural network models for transliteration
2
Neural Machine Translation by Jointly Learning to Align and Translate
3
Four Techniques for Online Handling of Out-of-Vocabulary Words in Arabic-English Statistical Machine Translation
4
Name Translation in Statistical Machine Translation - Learning When to Transliterate
5