Publication | Closed Access
Computing text similarity using Tree Edit Distance
25
Citations
8
References
2015
Year
Unknown Venue
EngineeringSimilarity MeasurePart-of-speech TaggingCorpus LinguisticsText MiningNatural Language ProcessingInformation RetrievalComputational LinguisticsGrammarLanguage StudiesSyntactic N-gramsMachine TranslationSimilarity SearchKnowledge DiscoveryComputer ScienceTree Edit DistanceSoft SimilarityTreebanksContent Similarity DetectionVector Space ModelText SimilarityLinguisticsSemantic Similarity
In this paper, we propose the application of the Tree Edit Distance (TED) for calculation of similarity between syntactic n-grams for further detection of soft similarity between texts. The computation of text similarity is the basic task for many natural language processing problems, and it is an open research field. Syntactic n-grams are text features for Vector Space Model construction extracted from dependency trees. Soft similarity is application of Vector Space Model taking into account similarity of features. First, we discuss the advantages of the application of the TED to syntactic n-grams. Then, we present a procedure based on the TED and syntactic n-grams for calculating soft similarity between texts.
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