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2020
- handle: 10670/1.j5ndsl
- hal: hal-03560385
info:eu-repo/semantics/OpenAccess
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Ludovic Tanguy et al., « LITL at SMM4H: an old-school feature-based classifier for identifying adverse effects in Tweets », HAL-SHS : linguistique, ID : 10670/1.j5ndsl
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This paper describes our participation to the SMM4H shared task 2. We designed a linear classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English and French, and is based on a number of ad-hoc word lists and features. These cues were mostly obtained through an extensive corpus analysis of the provided training data. Different weighting schemes were tested (manually tuned or based on a logistic regression), the best one achieving a F1 score of 0.31 for English and 0.15 for French.