AMRITA_CEN$@$SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders

Mahalakshmi Shanumuga Sundaram, Anand Kumar Madasamy, Soman Kotti Padannayil
2015 Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)  
We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Semantic Similarity in Twitter. Our paraphrase detection system makes use of phrase-structure parse tree embeddings that are then provided as input to a conventional supervised classification model. We achieve an F1 score of 0.45 on paraphrase identification and a Pearson correlation of 0.303 on computing semantic similarity.
doi:10.18653/v1/s15-2008 dblp:conf/semeval/SundaramMP15 fatcat:kv6ty7gyrncv3dl4cioiydvv7q