TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay
Mladen Karan, Goran Glavaš, Jan Šnajder, Bojana Dalbelo Bašić, Ivan Vulić, Marie‐Francine Moens · 2015
When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic simi-larity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic over-lap and word alignment features, previously shown to be effective for detecting semantic textual similarity. TweetingJay reaches 65.9% F1-score and ranked fourth among the 18 par-ticipating systems. We additionally provide an analysis of the dataset and point to some peculiarities of the evaluation setup. 1