IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized Mongue-Elkan Method for Approximate Text String Comparison
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Text comparison using soft cardinality
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
SC spectra: a linear-time soft cardinality approximation for text comparison
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
SemEval-2012 task 6: a pilot on semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
A new benchmark dataset with production methodology for short text semantic similarity algorithms
ACM Transactions on Speech and Language Processing (TSLP)
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We present an approach for the construction of text similarity functions using a parameterized resemblance coefficient in combination with a softened cardinality function called soft cardinality. Our approach provides a consistent and recursive model, varying levels of granularity from sentences to characters. Therefore, our model was used to compare sentences divided into words, and in turn, words divided into q-grams of characters. Experimentally, we observed that a performance correlation function in a space defined by all parameters was relatively smooth and had a single maximum achievable by "hill climbing." Our approach used only surface text information, a stop-word remover, and a stemmer to tackle the semantic text similarity task 6 at SEMEVAL 2012. The proposed method ranked 3rd (average), 5th (normalized correlation), and 15th (aggregated correlation) among 89 systems submitted by 31 teams.