acl acl2013 acl2013-222 acl2013-222-reference knowledge-graph by maker-knowledge-mining

222 acl-2013-Learning Semantic Textual Similarity with Structural Representations


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Author: Aliaksei Severyn ; Massimo Nicosia ; Alessandro Moschitti

Abstract: Measuring semantic textual similarity (STS) is at the cornerstone of many NLP applications. Different from the majority of approaches, where a large number of pairwise similarity features are used to represent a text pair, our model features the following: (i) it directly encodes input texts into relational syntactic structures; (ii) relies on tree kernels to handle feature engineering automatically; (iii) combines both structural and feature vector representations in a single scoring model, i.e., in Support Vector Regression (SVR); and (iv) delivers significant improvement over the best STS systems.


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