Semantic Similarity in Social Media

Winner of the SemEval 2015 shared task for detection of semantic similarity in Twitter. The goal was to build a system capable of judging if two texts express the same or very similar meaning. This task is complicated by the informal vocabulary and syntax employed online. Our novel approach succeeded in generalizing well to new topics perhaps because our recurrent neural network is equipped with both string matching metrics and pre-trained models of word meaning learned from large collections of online text.

We describe our approach in this paper: http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval002.pdf

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