AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP
| Autor: | Abu Bakr Soliman, Kareem Eissa, Samhaa R. El-Beltagy |
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| Rok vydání: | 2017 |
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Word embedding Artificial neural network business.industry Arabic Computer science 02 engineering and technology computer.software_genre Syntax language.human_language Set (abstract data type) 020204 information systems 0202 electrical engineering electronic engineering information engineering language General Earth and Planetary Sciences 020201 artificial intelligence & image processing Word2vec Artificial intelligence business computer Word (computer architecture) Natural language processing General Environmental Science |
| Zdroj: | ACLING |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2017.10.117 |
| Popis: | Advancements in neural networks have led to developments in fields like computer vision, speech recognition and natural language processing (NLP). One of the most influential recent developments in NLP is the use of word embeddings, where words are represented as vectors in a continuous space, capturing many syntactic and semantic relations among them. AraVec is a pre-trained distributed word representation (word embedding) open source project which aims to provide the Arabic NLP research community with free to use and powerful word embedding models. The first version of AraVec provides six different word embedding models built on top of three different Arabic content domains; Tweets, World Wide Web pages and Wikipedia Arabic articles. The total number of tokens used to build the models amounts to more than 3,300,000,000. This paper describes the resources used for building the models, the employed data cleaning techniques, the carried out preprocessing step, as well as the details of the employed word embedding creation techniques. |
| Databáze: | OpenAIRE |
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