For the syntactic trees, they can connect the anchor words to the functional words (i.e., negation, modal auxiliaries) that are far away, but convey important information to affect the factuality of the event mentions. For instance, the dependency tree of the sentence "I will, after seeing the treatment of others, go back when I need medical care." will be helpful to directly link the anchor word "go" to the modal auxiliary "will" to successfully predict the non-factuality of the event mention. Regarding the semantic information, the meaning of the some important context words in the sentences can contribute significantly to the factuality of an event mention. For example, in the sentence "Knight lied when he said I went to the ranch.", the meaning represented by the cue word "lied" is crucial to classify the event mention associated with the anchor word "went" as non-factual. The meaning of such cue words and their interactions with the anchor words can be captured via their distributed representations (i.e., with word embeddings and long-short term memory networks (LSTM))<cite> (Rudinger et al., 2018)</cite> .