A stack decoder approach to approximate string matching

Abstract

We present a new efficient algorithm for top-N match retrieval of sequential patterns. Our approach is based on an incremental approximation of the string edit distance using index information and a stack based search. Our approach produces hypotheses with average edit error of about 0.29 edits from the optimal SED result while using only about 5% of the CPU computation.

Towards Efficient Translation Memory Search Based on Multiple Sentence Signatures

“…This approach is important because it improves the accuracy of an off line system using a position index by using an approximation of the string edit distance without sacrificing speed. The results are within 2.5% error (Huerta, 2010b). In this paper, we will focus exclusively on the on-line approach.…”

“…To reduce this mismatch between off-line and on-line methods, it is possible to approximate the SED (and the related Longest Common Subsequence computation) based on a stack computation and information derived from a positional index. This computation is possible through the use of a Stack structure and a A* like algorithm as described in (Huerta, 2010b). In that paper, Huerta proposed a method that takes O(m s log s) operations on average where s is the depth of the stack (typically much smaller than T, or m) instead of O(T) using a positional index.…”

Subsequence similarity language models

“…In previous work [4,5] we evaluated the ability of a stack approach to recall an optimal match given a query, as well as the speed compared to naIve SED. We now want to investigate the question of n-gram equivalence, namely, to How does S2LM behave as compared to n-gram SLM in a LM rescoring task?…”