Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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ACM Computing Surveys (CSUR)
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Random Structures & Algorithms
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SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
New Algorithms for the Longest Common Subsequence Problem
New Algorithms for the Longest Common Subsequence Problem
EXTRA: a system for example-based translation assistance
Machine Translation
Sentence similarity measurement based on shallow parsing
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Recognising sentence similarity using similitude and dissimilarity features
International Journal of Advanced Intelligence Paradigms
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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
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Textual data is the main electronic form of knowledge representation. Sentences, meant as logic units of meaningful word sequences, can be considered its backbone. In this paper, we propose a solution based on a purely syntactic approach for searching similarities within sentences, named approximate sub2sequence matching. This process being very time consuming, efficiency in retrieving the most similar parts available in large repositories of textual data is ensured by making use of new filtering techniques. As far as the design of the system is concerned, we chose a solution that allows us to deploy approximate sub2 sequence matching without changing the underlying database.