SEMEVAL 2016

TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification

SEMEVAL 2016 balikasg/SemEval2016-Twitter_Sentiment_Evaluation

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.

SENTIMENT ANALYSIS

RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy

SEMEVAL 2016 didzis/tensorflowAMR

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.

AMR PARSING