Karaoglan B.Kisla T.Metin S.K.2023-06-162023-06-162018-059.78E+12https://doi.org/10.1109/SIU.2018.8404204https://hdl.handle.net/20.500.14365/3605Aselsan;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 -- 137780Automatic paraphrase identification is a natural language understanding problem where a decision is to be made whether the given sentence pairs bare similar meanings to a certain extent. Syntactic and semantic features are used to classify the sentences as paraphrase or non-paraphrase. Word overlapping, word ordering are some of the syntactic features widely used in the literature, where, similarity of words in meaning and named entity (NE) overlap are among the semantic features. Turkish, unfortunately doesn't have a useful tool like WordNet to draw the semantic relations between words as it is done for English. Here we exploit tense and polarity differences as semantic features and assess the improvement on the classification brought by these semantic features. We performed the experiments with several different combinations of features on the Turkish paraphrase corpus that is built by the researchers and report the results. © 2018 IEEE.trinfo:eu-repo/semantics/closedAccessParaphrase identificationSemantic featuresSentence based text similarityNatural language processing systemsSemanticsSignal processingSyntacticsNatural language understandingParaphrase identificationsSemantic attributeSemantic featuresSemantic relationsSimilarity of wordsSyntactic featuresText similarityClassification (of information)Conference Object10.1109/SIU.2018.84042042-s2.0-85050822117