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Arabic Named Entity Recognition using Word Representations
[article]
2018
arXiv
pre-print
Recent work has shown the effectiveness of the word representations features in significantly improving supervised NER for the English language. In this study we investigate whether word representations can also boost supervised NER in Arabic. We use word representations as additional features in a Conditional Random Field (CRF) model and we systematically compare three popular neural word embedding algorithms (SKIP-gram, CBOW and GloVe) and six different approaches for integrating word
arXiv:1804.05630v1
fatcat:ctioav6jcvghlldrklzrg3tmaq