In this paper, we describe TAMU's system submitted to the TAC KBP 2017 event
nugget detection and coreference resolution task. Our system builds on the
statistical and empirical observations made on training and development data.
We found that modifiers of event nuggets tend to have unique syntactic
distribution. Their parts-of-speech tags and dependency relations provides them
essential characteristics that are useful in identifying their span and also
defining their types and realis status. We further found that the joint
modeling of event span detection and realis status identification performs
better than the individual models for both tasks. Our simple system designed
using minimal features achieved the micro-average F1 scores of 57.72, 44.27 and
42.47 for event span detection, type identification and realis status
classification tasks respectively. Also, our system achieved the CoNLL F1 score
of 27.20 in event coreference resolution task.