"Easy" meta-embedding for detecting and correcting semantic errors in Arabic documents

Word-Embedding models have enabled massive advances in natural language understanding tasks and achieved state-of-the-art performances in multiple natural language processing tasks. In this paper, we present an original method based on an "easy" meta-embedding to automatically detect and correct Arabic real-words errors that are semantically inconsistent with the context of the sentence. Due to the lexical proximity of words in Arabic, the risk of having this type of errors in documents is relatively high compared to other languages. Our method uses three word embedding techniques and their combination, namely SkipGram, FastText and BERT for both detection and correction. It checks the semantic affinity of words with the immediate context in a collocation and the near context of the sentence. Experiments have shown that the proposed meta-embedding improves the overall performance of our system.