This paper describes MITRE's participation in the Paraphrase and Semantic Similarity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similarity and second in Paraphrase Identification with scores of Pearson's r of 61.9%, F1 of 66.7%, and maxF1 of 72.4%. We detail the approaches we explored including mixtures of string matching metrics, alignments using tweet-specific distributed word representations, recurrent neural networks for modeling similarity with those alignments, and distance measurements on pooled latent semantic features. Logistic regression is used to tie the systems together into the ensembles submitted for evaluation.
Zarrella et al. REF present an ensemble approach based on various features such as mixtures of string matching metrics, distance measurements, tweet-specific distributed word representations, and recurrent neural networks for modeling similarity.
MITRE: Seven Systems for Semantic Similarity in Tweets
SemEval@NAACL-HLT
Computer Science