Abstract: There are a great deal of negative and speculative expressions in natural language texts. Identifying such information and separating them from the affirmative content plays a critical role in a variety of downstream applications of natural language processing, such as information extraction, information retrieval, and sentiment analysis. Compared with that in English, current research on negative and speculative scope detection for Chinese is scarce. In this paper, we come up with a fusion model based on bidirectional long-term memory (BiLSTM) networks and conditional random fields (CRF), and recast the scope detection problem as a sequence-labeling task. Given a negative or speculative keyword, we need to identify its semantic scope in sentence. This model can learn not only the forward and backward context information by LSTM networks but also the dependency relationship between the output labels via a CRF layer, which is motivated by the superiority of sequential architecture in effectively encoding order information and long-range context dependency. The experimental results on CNeSp corpus show the effectiveness of our proposed model. On the financial dataset, our approach achieves the performance of 79.16% and 76.79% with the improvements of 25.06% and 34.46% for negation and speculation, respectively, compared with the state-of-the-art.