Negation and Speculation Target Identification

Negation and speculation are common in natural language text. Many applications, such as biomedical text mining and clinical information extraction, seek to distinguish positive/factual objects from negative/speculative ones (i.e., to determine what is negated or speculated) in biomedical texts. This paper proposes a novel task, called negation and speculation target identification, to identify the target of a negative or speculative expression. For this purpose, a new layer of the target information is incorporated over the BioScope corpus and a machine learning algorithm is proposed to automatically identify this new information. Evaluation justifies the effectiveness of our proposed approach on negation and speculation target identification in biomedical texts.