Abstract
Determining the textual entailment between texts is important in many NLP tasks, such as summarization, question answering, and information extraction and retrieval. Various methods have been suggested based on external knowledge sources; however, such resources are not always available in all languages and their acquisition is typically laborious and very costly. Distributional word representations such as word embeddings learned over large corpora have been shown to capture syntactic and semantic word relationships. Such models have contributed to improving the performance of several NLP tasks. In this paper, we address the problem of textual entailment in Arabic. We employ both traditional features and distributional representations. Crucially, we do not depend on any external resources in the process. Our suggested approach yields state of the art performance on a standard data set, ArbTE, achieving an accuracy of 76.2 % compared to state of the art of 69.3 %.- Anthology ID:
- W17-1322
- Volume:
- Proceedings of the Third Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Nizar Habash, Mona Diab, Kareem Darwish, Wassim El-Hajj, Hend Al-Khalifa, Houda Bouamor, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- SEMITIC
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 185–190
- Language:
- URL:
- https://aclanthology.org/W17-1322
- DOI:
- 10.18653/v1/W17-1322
- Cite (ACL):
- Nada Almarwani and Mona Diab. 2017. Arabic Textual Entailment with Word Embeddings. In Proceedings of the Third Arabic Natural Language Processing Workshop, pages 185–190, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Arabic Textual Entailment with Word Embeddings (Almarwani & Diab, WANLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/W17-1322.pdf