Abstract: The separated words, as a unique grammatical phenomenon in Chinese, refers to the components of words can be divided by other grammatical constituents without changing the word meaning. This paper proposes a character-level sequence tagging method for automatic recognition of separated two-character verbs via the MASK mechanism. We mask the separated words, and assign different embeddings to the first and second components of a word to emphasize the morpheme sequence order. A double coding model is designed to encode the original sentence and the masked sentence, respectively. Experimental results show that the proposed BERT_MASK + 2BiLSTMs + CRF model improves the F₁-value by 2.85% compared with the state-of-the-art model.