Specifically, we participated in Task 4, namely "Sentiment Analysis in Twitter" for which we implemented sentiment classification systems for subtasks A, B, C and D. Our approach consists of two steps.
We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering.
The first extension com-bines the smatch scoring script with the C6. 0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs.
We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets.