Causal Explanation Analysis on Social Media
Table 7: The effect of Linear SVM Cauality model (CP) within our pipeline. CEIall: LSTM CEI models trained on all messages; CEIcausal: LSTM CEI models trained only on causality messages (CEIcausal); CP + CEIall|causal: the combination of Linear SVM and each LSTM model. Bold: significant (p
['[BOLD] Model', 'Prec', 'Rec', 'F1']
Though all setups performed decent (F1>0.81) we see that the pipelined approach, first predicting causality (with the linear SVM) and then predicting causal explanations only for those with marked causal (CP + CEIcausal) yielded the strongest results. This also utilized the CEI model only trained on those annotated causal. Besides performance, an added benefit from this two step approach is that the CP step is less computational intensive of the CEI step and approximately 2/3 of documents will never need the CEI step applied.