A Practical q -Gram Index for Text Retrieval Allowing Errors

We propose an indexing technique for approximate text searching, which is practical and powerful, and especially optimized for natural language text. Unlike other indices of this kind, it is able to retrieve any string that approximately matches the search pattern, not only words. Every text substri...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gonzalo Navarro [verfasserIn] Ricardo Baeza-Yates [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Übergeordnetes Werk:	In: CLEI Electronic Journal - Centro Latinoamericano de Estudios en Informática, 2008, 1(2018), 2
Übergeordnetes Werk:	volume:1 ; year:2018 ; number:2

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.19153/cleiej.1.2.3

Katalog-ID:	DOAJ033423946

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author	Gonzalo Navarro
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topic_title	QA75.5-76.95 A Practical q -Gram Index for Text Retrieval Allowing Errors
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title	A Practical q -Gram Index for Text Retrieval Allowing Errors
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title_sort	practical q -gram index for text retrieval allowing errors
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title_auth	A Practical q -Gram Index for Text Retrieval Allowing Errors
abstract	We propose an indexing technique for approximate text searching, which is practical and powerful, and especially optimized for natural language text. Unlike other indices of this kind, it is able to retrieve any string that approximately matches the search pattern, not only words. Every text substring of a fixed length q is stored in the index, together with pointers to all the text positions where it appears. The search pattern is partitioned into pieces which are searched in the index, and all their occurrences in the text are verified for a complete match. To reduce space requirements, pointers to blocks instead of exact positions can be used, which increases querying costs. We design an algorithm to optimize the pattern partition into pieces so that the total number of verifications is minimized. This is especially well suited for natural language texts, and allows to know in advance the expected cost of the search and the expected relevance of the query to the user. We show experimentally the building time, space requirements and querying time of our index, finding that it is a practical alternative for text retrieval. The retrieval times are reduced from 10% to 60% of the best on-line algorithm.
abstractGer	We propose an indexing technique for approximate text searching, which is practical and powerful, and especially optimized for natural language text. Unlike other indices of this kind, it is able to retrieve any string that approximately matches the search pattern, not only words. Every text substring of a fixed length q is stored in the index, together with pointers to all the text positions where it appears. The search pattern is partitioned into pieces which are searched in the index, and all their occurrences in the text are verified for a complete match. To reduce space requirements, pointers to blocks instead of exact positions can be used, which increases querying costs. We design an algorithm to optimize the pattern partition into pieces so that the total number of verifications is minimized. This is especially well suited for natural language texts, and allows to know in advance the expected cost of the search and the expected relevance of the query to the user. We show experimentally the building time, space requirements and querying time of our index, finding that it is a practical alternative for text retrieval. The retrieval times are reduced from 10% to 60% of the best on-line algorithm.
abstract_unstemmed	We propose an indexing technique for approximate text searching, which is practical and powerful, and especially optimized for natural language text. Unlike other indices of this kind, it is able to retrieve any string that approximately matches the search pattern, not only words. Every text substring of a fixed length q is stored in the index, together with pointers to all the text positions where it appears. The search pattern is partitioned into pieces which are searched in the index, and all their occurrences in the text are verified for a complete match. To reduce space requirements, pointers to blocks instead of exact positions can be used, which increases querying costs. We design an algorithm to optimize the pattern partition into pieces so that the total number of verifications is minimized. This is especially well suited for natural language texts, and allows to know in advance the expected cost of the search and the expected relevance of the query to the user. We show experimentally the building time, space requirements and querying time of our index, finding that it is a practical alternative for text retrieval. The retrieval times are reduced from 10% to 60% of the best on-line algorithm.
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title_short	A Practical q -Gram Index for Text Retrieval Allowing Errors
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