Soleymanzadeh K.Karaoğlan B.Metin S.K.Kişla T.2023-06-162023-06-1620189.78E+12https://doi.org/10.1109/SIU.2018.8404633https://hdl.handle.net/20.500.14365/3613Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- 137780Paraphrase identification (PI) is to recognize whether given two sentences are restatements of each other or not. In our study we propose an approach that exploits machine translation and text similarity metrics as features for PI. Machine learning algorithms like Support Vector Machine (SVM) with three different kernels, C4.5 Decision tree and Multinomial Naïve Bayes (NB) are trained with these features. We evaluated our system on Parder, Turkish paraphrase corpus. The experimental results show that the proposed approach offers state-of-the-art results. © 2018 IEEE.eninfo:eu-repo/semantics/closedAccessMachine translation metricsParaphrase IdentificationText similarity metricsBarium compoundsComputational linguisticsComputer aided language translationData miningDecision treesImage retrievalLearning algorithmsSodium compoundsSupport vector machinesC4.5 decision treesMachine translationsMultinomialsParaphrase corpusParaphrase identificationsState of the artText similarityTurkishsSignal processingConference Object10.1109/SIU.2018.84046332-s2.0-85050799621