Named Entity Recognition Using Word-Embedding Techniques for ArabicWeb16: An Empirical Study

The 3rd Workshop on Open-Source Arabic Corpora and Processing Tools introduces ArabicWeb16 Data Challenge track. The challenge is about experimenting with ArabicWeb16 dataset, the largest Arabic Web dataset publicly available with about 150M Arabic Web pages. In this paper, we explore the ArabicWeb16 dataset and experiment with it to build word-embedding models for Named Entity Recognition (NER) task. Word-embedding models are powerful for building many Natural Language Processing (NLP) tasks including NER. We tried two word-embedding models: Google Word2Vec model and Stanford GloVe model. The two models were used to recognize similar words for each named entity type. The ArabicWeb16 dataset was somehow hard to pre-process, however, the final results showed promising outputs.