Unsupervised extraction of text segments from heterogeneous document collections

Hong Cui

doi:10.1002/meet.14504701355

Unsupervised extraction of text segments from heterogeneous document collections

Hong Cui

Information Resources and Library Science, School of

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

This paper describes a simple, unsupervised bootstrapping procedure that identifies morphological description segments from heterogeneous biodiversity document collections. While the procedure is used to preprocess biodiversity literature for semantic annotation of morphological descriptions in our project, it also can be used to crawl the Web for morphological descriptions for a biodiversity niche search engine.

Original language	English (US)
Journal	Proceedings of the ASIST Annual Meeting
Volume	47
DOIs	https://doi.org/10.1002/meet.14504701355
State	Published - Nov 1 2010

Keywords

Biodiversity document collections
Morphological description
Segment information retrieval
Semantic annotation
Unsupervised machine learning

ASJC Scopus subject areas

Information Systems
Library and Information Sciences

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10.1002/meet.14504701355

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title = "Unsupervised extraction of text segments from heterogeneous document collections",

abstract = "This paper describes a simple, unsupervised bootstrapping procedure that identifies morphological description segments from heterogeneous biodiversity document collections. While the procedure is used to preprocess biodiversity literature for semantic annotation of morphological descriptions in our project, it also can be used to crawl the Web for morphological descriptions for a biodiversity niche search engine.",

keywords = "Biodiversity document collections, Morphological description, Segment information retrieval, Semantic annotation, Unsupervised machine learning",

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KW - Biodiversity document collections

KW - Morphological description

KW - Segment information retrieval

KW - Semantic annotation

KW - Unsupervised machine learning

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JF - Proceedings of the ASIST Annual Meeting

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Unsupervised extraction of text segments from heterogeneous document collections

Abstract

Keywords

ASJC Scopus subject areas

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