Constrained Permutations for Computing Textual Similarity

A M Ramsay; A Alshahrani

doi:10.1007/978-3-319-99344-78

Constrained Permutations for Computing Textual Similarity

A M Ramsay, A Alshahrani

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

A wide range of algorithms for computing textual similarity have been proposed. Much recent work has been aimed at calculating lexical similarity, but in general such calculations have to be treated as components in larger algorithms for computing similarity between sentences.

In the current paper we describe a refinement of the well-known dynamic-time warping (DTW) algorithm for calculating the string edit distance between a pair of texts. The refined version of this algorithm allows for a range of constrained permutations without increasing the complexity of the underlying algorithm.

Original language	English
Title of host publication	Artificial Intelligence:
Subtitle of host publication	Methodology, Systems, and Applications - 18th International Conference, AIMSA 2018, Varna, Bulgaria, September 12-14, 2018, Proceedings
Pages	83-92
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-319-99344-78
Publication status	Published - 28 Aug 2018

Access to Document

10.1007/978-3-319-99344-78

https://doi.org/10.1007/978-3-319-99344-78

Cite this

@inproceedings{b9085f01224b43599fe086d998fcf962,

title = "Constrained Permutations for Computing Textual Similarity",

abstract = "A wide range of algorithms for computing textual similarity have been proposed. Much recent work has been aimed at calculating lexical similarity, but in general such calculations have to be treated as components in larger algorithms for computing similarity between sentences.In the current paper we describe a refinement of the well-known dynamic-time warping (DTW) algorithm for calculating the string edit distance between a pair of texts. The refined version of this algorithm allows for a range of constrained permutations without increasing the complexity of the underlying algorithm.",

author = "Ramsay, {A M} and A Alshahrani",

year = "2018",

month = aug,

day = "28",

doi = "10.1007/978-3-319-99344-78",

language = "English",

pages = "83--92",

booktitle = "Artificial Intelligence:",

}

TY - GEN

T1 - Constrained Permutations for Computing Textual Similarity

AU - Ramsay, A M

AU - Alshahrani, A

PY - 2018/8/28

Y1 - 2018/8/28

N2 - A wide range of algorithms for computing textual similarity have been proposed. Much recent work has been aimed at calculating lexical similarity, but in general such calculations have to be treated as components in larger algorithms for computing similarity between sentences.In the current paper we describe a refinement of the well-known dynamic-time warping (DTW) algorithm for calculating the string edit distance between a pair of texts. The refined version of this algorithm allows for a range of constrained permutations without increasing the complexity of the underlying algorithm.

AB - A wide range of algorithms for computing textual similarity have been proposed. Much recent work has been aimed at calculating lexical similarity, but in general such calculations have to be treated as components in larger algorithms for computing similarity between sentences.In the current paper we describe a refinement of the well-known dynamic-time warping (DTW) algorithm for calculating the string edit distance between a pair of texts. The refined version of this algorithm allows for a range of constrained permutations without increasing the complexity of the underlying algorithm.

UR - https://link.springer.com/chapter/10.1007/978-3-319-99344-7_8

U2 - 10.1007/978-3-319-99344-78

DO - 10.1007/978-3-319-99344-78

M3 - Conference contribution

SP - 83

EP - 92

BT - Artificial Intelligence:

ER -

Constrained Permutations for Computing Textual Similarity

Abstract

Access to Document

Other files and links

Fingerprint

Cite this