motion(a,t)$CoPA(c)- &3p(c,a)5%) • By topic, nearest neighbors (KNN) motion$"243+,1&3(Dor+, 2018)motion5& • By topic, word2vec features (W2V) motion(a, t).topic5w2v*Embedding >:;=7<8 *;96 • By topic, Naive Bayes (NB) and Recurrent Neural Network (RNN) t- /43 5 0CoPA5+%)NB, RNN • By topic and action (LR): feature engineering*17.feature5(' • Ensemble 6..,#*!;96.CoPA5 12