Speculation and Negation detection in French biomedical corpora
In this work, we propose to address the detection of negation and speculation, and of their scope, in French biomedical documents. It has been indeed observed that they play an important role and provide crucial clues for other NLP applications. Our methods are based on CRFs and BiLSTM. We reach up to 97.21 {\%} and 91.30 {\%} F-measure for the detection of negation and speculation cues, respectively, using CRFs. For the computing of scope, we reach up to 90.81 {\%} and 86.73 {\%} F-measure on negation and speculation, respectively, using BiLSTM-CRF fed with word embeddings.
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