Automated indexing with thesaurus descriptors: A co-occurrence based approach to multilingual retrieval

Ferber, Reginald

doi:10.1007/bfb0026731

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…Ferber [6] developed a system with a linear associative model that was based on titles (short text) and co-occurrence data between words and descriptors. He reported encouraging results but noted that titles were sometimes insufficient and that it was unclear if the cooccurrence approach generalizes to different domains.…”

Section: Statistical Associative Approachesmentioning

confidence: 99%

“…It denotes the process of describing the contents of documents with appropriate concepts from a controlled vocabulary in accordance with certain criteria. It aims to cover the main topics exhaustively and describe them as precisely as possible, while seeking a condensed representation of the content that contains, for instance, roughly 5 to 8 concepts on average 6 [25,20,18,11]. Automatic subject indexing attempts to implement this task algorithmically.…”

Section: Subject Indexingmentioning

confidence: 99%

“…Compared to manual indexing, automatic indexing faces several challenges: First, legal restrictions might prevent the usage of publication full-text and/or abstracts, which leads to little information available to the indexing approach and thus decreases performance [6]. Second, the distribution of concepts in the training data set can be very skewed and some concepts might not appear at all [25].…”

Section: Introductionmentioning

confidence: 99%

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Fusion architectures for automatic subject indexing under concept drift

Toepfer

Seifert

2018

Int J Digit Libr

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Indexing documents with controlled vocabularies enables a wealth of semantic applications for digital libraries. Due to the rapid growth of scientific publications, machine learning based methods are required that assign subject descriptors automatically. While stability of generative processes behind the underlying data is often assumed tacitly, it is being violated in practice. Addressing this problem, this article studies explicit and implicit concept drift, that is, settings with new descriptor terms and new types of documents, respectively. First, the existence of concept drift in automatic subject indexing is discussed in detail and demonstrated by example. Subsequently, architectures for automatic indexing are analysed in this regard, highlighting individual strengths and weaknesses. The results of the theoretical analysis justify research on fusion of different indexing approaches with special consideration on information sharing among descriptors. Experimental results on titles and author keywords in the domain of economics underline the relevance of the fusion methodology, especially under concept drift. Fusion approaches outperformed non-fusion strategies on the tested data sets, which comprised shifts in priors of descriptors as well as covariates. These findings can help researchers and practitioners in digital libraries to

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Section: Statistical Associative Approachesmentioning

confidence: 99%

Section: Subject Indexingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fusion architectures for automatic subject indexing under concept drift

Toepfer

Seifert

2018

Int J Digit Libr

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“…Therefore, techniques for automatically annotating documents with terms from a thesaurus have received significant attention recently (cf. Clifford Gay and Aronson, 2005;Stuckenschmidt et al, 2004;Ferber, 1997). Figure 1 presents the architecture of a typical thesaurusbased semantic search system.…”

Section: Motivationmentioning

confidence: 99%

Assessing thesaurus-based annotations for semantic search applications

Eckert

Pfeffer

Stuckenschmidt

2008

IJMSO

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Statistical methods for automated document indexing are becoming an alternative to the manual assignment of keywords. We argue that the quality of the thesaurus used as a basis for indexing in regard to its ability to adequately cover the contents to be indexed and as a basis for the specific indexing method used is of crucial importance in automatic indexing. We present an interactive tool for thesaurus evaluation that is based on a combination of statistical measures and appropriate visualisation techniques that supports the detection of potential problems in a thesaurus. We describe the methods used and show that the tool supports the detection and correction of errors, leading to a better indexing result.

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“…Their application was on automatic indexing mainly, [5,16] but they have been used for term expansion and clustering also. [17] The conditional probability and the equivalence index achieved some of the best results.…”

Section: Fig 2 Parallel Corpus Simulation With Vague Abstract and Fmentioning

confidence: 99%

Meta-data Extraction and Query Translation. Treatment of Semantic Heterogeneity

Strötgen¹

2002

Research and Advanced Technology for Digital Libraries

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Abstract. The project CARMEN 1 ("Content Analysis, Retrieval and Metadata: Effective Networking") aimed among other goals at improving the expansion of searches in bibliographic databases into Internet searches. We pursued a set of different approaches to the treatment of semantic heterogeneity (meta-data extraction, query translation using statistic relations and cross-concordances). This paper describes the concepts and implementation of this approaches and the evaluation of the impact for the retrieval result.

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Automated indexing with thesaurus descriptors: A co-occurrence based approach to multilingual retrieval

Cited by 8 publications

References 14 publications

Fusion architectures for automatic subject indexing under concept drift

Fusion architectures for automatic subject indexing under concept drift

Assessing thesaurus-based annotations for semantic search applications

Meta-data Extraction and Query Translation. Treatment of Semantic Heterogeneity

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