Algorithms for approximate string matching
Information and Control
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Synchronized rational relations of finite and infinite words
Theoretical Computer Science - Selected papers of the International Colloquium on Words, Languages and Combinatorics, Kyoto, Japan, August 1990
Deterministic part-of-speech tagging with finite-state transducers
Computational Linguistics
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The String-to-String Correction Problem
Journal of the ACM (JACM)
Fast Approximate Search in Large Dictionaries
Computational Linguistics
The growth ratio of synchronous rational relations is unique
Theoretical Computer Science
Fast Selection of Small and Precise Candidate Sets from Dictionaries for Text Correction Tasks
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Computation of similarity: similarity search as computation
CiE'11 Proceedings of the 7th conference on Models of computation in context: computability in Europe
WallBreaker: overcoming the wall effect in similarity search
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Hi-index | 5.23 |
Given some form of distance between words, a fundamental operation is to decide whether the distance between two given words w and v is within a given bound. In earlier work, we introduced the concept of a universal Levenshtein automaton for a given distance bound n. This deterministic automaton takes as input a sequence @g of bitvectors computed from w and v. The sequence @g is accepted iff the Levenshtein distance between w and v does not exceed n. The automaton is called universal since the same automaton can be used for arbitrary input words w and v, regardless of the underlying input alphabet. Here, we extend this picture. After introducing a large abstract family of generalized word distances, we exactly characterize those members where word neighborhood can be decided using universal neighborhood automata similar to universal Levenshtein automata. Our theoretical results establish several bridges to the theory of synchronized finite-state transducers and dynamic programming. For small neighborhood bounds, universal neighborhood automata can be held in main memory. This leads to very efficient algorithms for the above decision problem. Evaluation results show that these algorithms are much faster than those based on dynamic programming.