<b>Automated subject classification of textual Web pages, based on a controlled vocabulary: Challenges and recommendations</b>

Golub, Koraljka

doi:10.1080/13614560600774313

Cited by 22 publications

(15 citation statements)

References 11 publications

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“…This is in compliance with previous results of the algorithm's performance, based on a pre-classified collection of research abstracts, where it was shown that certain classes have better performance than others (Golub et al, 2007).…”

Section: Discussionsupporting

confidence: 76%

“…The fact that evaluations between the four tasks differed is in line with a previous study, where the algorithm's performance was tested on a pre-classified collection of research abstracts (Golub et al, 2007). The study showed that certain classes performed better than others.…”

Section: Automatically Assigned Classes As Judged By the User Study Psupporting

confidence: 70%

“…When a string is found, the class which it designates is assigned to the web page. Performance results for the algorithm on a collection of paper abstracts were reported as comparable to state-of-the-art algorithms in machine learning, especially for certain classes (Golub et al, 2007).…”

mentioning

confidence: 73%

“…Table 10. Performance for the four classes on the collection of paper abstracts (described in Golub et al, 2007), with the same parameters as in this study. …”

Section: Automatically Assigned Classes As Judged By the User Study Pmentioning

confidence: 99%

“…Findings and setting that had proven best in a previous experiment were applied (Golub et al, 2007). Weights 1, 3, and 4 for single, phrase or Boolean term were multiplied by the weight for the type of class to which the term mapped, 1 or 2 for optional or main class, and by the weight for the type of Ei term (broader 1, narrower 2, preferred 4, related 1, synonyms 3 and captions 4).…”

Section: Classification Algorithmmentioning

confidence: 99%

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Automated classification of web pages in hierarchical browsing

Golub

Lykke

2009

Journal of Documentation

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Section: Discussionsupporting

confidence: 76%

Section: Automatically Assigned Classes As Judged By the User Study Psupporting

confidence: 70%

mentioning

confidence: 73%

“…Table 10. Performance for the four classes on the collection of paper abstracts (described in Golub et al, 2007), with the same parameters as in this study. …”

Section: Automatically Assigned Classes As Judged By the User Study Pmentioning

confidence: 99%

Section: Classification Algorithmmentioning

confidence: 99%

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Automated classification of web pages in hierarchical browsing

Golub

Lykke

2009

Journal of Documentation

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Domain‐independent automatic keyphrase indexing with small training sets

Medelyan

Witten

2008

J. Am. Soc. Inf. Sci.

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Keyphrases are widely used in both physical and digital libraries as a brief but precise summary of documents. They help organize material based on content, provide thematic access, represent search results, and assist with navigation. Manual assignment is expensive, because trained human indexers must reach an understanding of the document and select appropriate descriptors according to defined cataloguing rules. We propose a new method that enhances automatic keyphrase extraction by using semantic information about terms and phrases gleaned from a domain-specific thesaurus. The key advantage of the new approach is that it performs well with very little training data. We evaluate it on a large set of manually indexed documents in the domain of agriculture, compare its consistency with a group of six professional indexers, and explore its performance on smaller collections of documents in other domains and of French and Spanish documents.

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An extensive study on automated Dewey Decimal Classification

Wang

2009

J. Am. Soc. Inf. Sci.

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In this paper, we present a theoretical analysis and extensive experiments on the automated assignment of Dewey Decimal Classification (DDC) classes to bibliographic data with a supervised machine-learning approach. Library classification systems, such as the DDC, impose great obstacles on state-of-art text categorization (TC) technologies, including deep hierarchy, data sparseness, and skewed distribution. We first analyze statistically the document and category distributions over the DDC, and discuss the obstacles imposed by bibliographic corpora and library classification schemes on TC technology. To overcome these obstacles, we propose an innovative algorithm to reshape the DDC structure into a balanced virtual tree by balancing the category distribution and flattening the hierarchy. To improve the classification effectiveness to a level acceptable to real-world applications, we propose an interactive classification model that is able to predict a class of any depth within a limited number of user interactions. The experiments are conducted on a large bibliographic collection created by the Library of Congress within the science and technology domains over 10 years. With no more than three interactions, a classification accuracy of nearly 90% is achieved, thus providing a practical solution to the automatic bibliographic classification problem.

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Automated subject classification of textual Web pages, based on a controlled vocabulary: Challenges and recommendations

Cited by 22 publications

References 11 publications

Automated classification of web pages in hierarchical browsing

Automated classification of web pages in hierarchical browsing

Domain‐independent automatic keyphrase indexing with small training sets

An extensive study on automated Dewey Decimal Classification

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