Abstract
The Arabic language has a rich morphology and structure with a diverse vocabulary and rarely used words. Consequently, most Arabic Natural Language Processing (NLP) tasks could benefit from embedding models that do not assign a distinct vector to each unique word in the used vocabulary but instead focus on the internal structure of words. The semantic meaning of a word is related to the meaning of its composing characters which contain rich internal information. In this paper, we propose a new embedding model using two levels of granularity; words and characters. Moreover, we describe the details of generating an Arabic word embeddings using Word2Vec and FastText models. Furthermore, a Deep Learning (DL) architecture will be applied to the top of the word-character embeddings. Experimental results show that the proposed scheme outperforms the state-of-the-art methods proposed for Arabic chatbots.
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Bensalah, N., Ayad, H., Adib, A., Ibn el farouk, A. (2022). Combining Word and Character Embeddings for Arabic Chatbots. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1417. Springer, Cham. https://doi.org/10.1007/978-3-030-90633-7_48
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DOI: https://doi.org/10.1007/978-3-030-90633-7_48
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