@inproceedings{almarwani-diab-2017-arabic,
    title = "{A}rabic Textual Entailment with Word Embeddings",
    author = "Almarwani, Nada  and
      Diab, Mona",
    editor = "Habash, Nizar  and
      Diab, Mona  and
      Darwish, Kareem  and
      El-Hajj, Wassim  and
      Al-Khalifa, Hend  and
      Bouamor, Houda  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-1322/",
    doi = "10.18653/v1/W17-1322",
    pages = "185--190",
    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 {\%}."
}