def _trainTimex(timexFeatures, timexLabels, grid=False): """ Purpose: Train a classifer for Timex3 expressions @param tokenVectors: A list of tokens represented as feature dictionaries @param Y: A list of lists of Timex3 classifications for each token in each sentence """ assert len(timexFeatures) == len(timexLabels), "{} != {}".format(len(timexFeatures), len(timexLabels)) Y = [l["entity_label"] for l in timexLabels] clf, vec = train_classifier(timexFeatures, Y, do_grid=grid, ovo=True) return clf, vec
def _trainEventClass(eventClassFeatures, eventClassLabels, grid=False): """ Model::_trainEventClass() Purpose: Train a classifer for event class identification @param tokenVectors: A list of tokens represented as feature dictionaries @param Y: A list of lists of event classifications for each token, with one list per sentence """ assert len(eventClassFeatures) == len(eventClassLabels), "{} != {}".format(len(eventClassFeatures), len(eventClassLabels)) Y = [l["entity_label"] for l in eventClassLabels] clf, vec = train_classifier(eventClassFeatures, Y, do_grid=grid) return clf, vec
def _trainTlink(tokenVectors, Y, grid=False): """ Model::_trainRelation() Purpose: Train a classifer for temporal relations between events and timex3 labels @param tokenVectors: A list of tokens represented as feature dictionaries @param Y: A list of relation classifications for each pair of timexes and events. """ print len(tokenVectors) print len(Y) assert len(tokenVectors) == len(Y) clf, vec = train_classifier(tokenVectors, Y, do_grid=grid, ovo=True) return clf, vec