Search (133 results, page 1 of 7)

  • × theme_ss:"Automatisches Indexieren"
  1. Gomez, I.: Coping with the problem of subject classification diversity (1996) 0.05 
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    Abstract
    The delimination of a research field in bibliometric studies presents the problem of the diversity of subject classifications used in the sources of input and output data. Classification of documents according the thematic codes or keywords is the most accurate method, mainly used is specialized bibliographic or patent databases. Classification of journals in disciplines presents lower specifity, and some shortcomings as the change over time of both journals and disciplines and the increasing interdisciplinarity of research. Standardization of subject classifications emerges as an important point in bibliometric studies in order to allow international comparisons, although flexibility is needed to meet the needs of local studies
  2. Golub, K.: Automated subject indexing : an overview (2021) 0.04 
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    Abstract
    In the face of the ever-increasing document volume, libraries around the globe are more and more exploring (semi-) automated approaches to subject indexing. This helps sustain bibliographic objectives, enrich metadata, and establish more connections across documents from various collections, effectively leading to improved information retrieval and access. However, generally accepted automated approaches that are functional in operative systems are lacking. This article aims to provide an overview of basic principles used for automated subject indexing, major approaches in relation to their possible application in actual library systems, existing working examples, as well as related challenges calling for further research.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.702-719
  3. Ward, M.L.: ¬The future of the human indexer (1996) 0.03 
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    Abstract
    Considers the principles of indexing and the intellectual skills involved in order to determine what automatic indexing systems would be required in order to supplant or complement the human indexer. Good indexing requires: considerable prior knowledge of the literature; judgement as to what to index and what depth to index; reading skills; abstracting skills; and classification skills, Illustrates these features with a detailed description of abstracting and indexing processes involved in generating entries for the mechanical engineering database POWERLINK. Briefly assesses the possibility of replacing human indexers with specialist indexing software, with particular reference to the Object Analyzer from the InTEXT automatic indexing system and using the criteria described for human indexers. At present, it is unlikely that the automatic indexer will replace the human indexer, but when more primary texts are available in electronic form, it may be a useful productivity tool for dealing with large quantities of low grade texts (should they be wanted in the database)
    Date
    9. 2.1997 18:44:22
  4. Micco, M.; Popp, R.: Improving library subject access (ILSA) : a theory of clustering based in classification (1994) 0.03 
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    Abstract
    The ILSA prototype was developed using an object-oriented multimedia user interfcae on six NeXT workstations with two databases: the first with 100.000 MARC records and the second with 20.000 additional records enhanced with table of contents data. The items are grouped into subject clusters consisting of the classification number and the first subject heading assigned. Every other distinct keyword in the MARC record is linked to the subject cluster in an automated natural language mapping scheme, which leads the user from the term entered to the controlled vocabulary of the subject clusters in which the term appeared. The use of a hierarchical classification number (Dewey) makes it possible to broaden or narrow a search at will
  5. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.03 
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    Abstract
    Topic models often produce unexplainable topics that are filled with noisy words. The reason is that words in topic modeling have equal weights. High frequency words dominate the top topic word lists, but most of them are meaningless words, e.g., domain-specific stopwords. To address this issue, in this paper we aim to investigate how to weight words, and then develop a straightforward but effective term weighting scheme, namely entropy weighting (EW). The proposed EW scheme is based on conditional entropy measured by word co-occurrences. Compared with existing term weighting schemes, the highlight of EW is that it can automatically reward informative words. For more robust word weight, we further suggest a combination form of EW (CEW) with two existing weighting schemes. Basically, our CEW assigns meaningless words lower weights and informative words higher weights, leading to more coherent topics during topic modeling inference. We apply CEW to Dirichlet multinomial mixture and latent Dirichlet allocation, and evaluate it by topic quality, document clustering and classification tasks on 8 real world data sets. Experimental results show that weighting words can effectively improve the topic modeling performance over both short texts and normal long texts. More importantly, the proposed CEW significantly outperforms the existing term weighting schemes, since it further considers which words are informative.
  6. Martins, A.L.; Souza, R.R.; Ribeiro de Mello, H.: ¬The use of noun phrases in information retrieval : proposing a mechanism for automatic classification (2014) 0.03 
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    Abstract
    This paper presents a research on syntactic structures known as noun phrases (NP) being applied to increase the effectiveness and efficiency of the mechanisms for the document's classification. Our hypothesis is the fact that the NP can be used instead of single words as a semantic aggregator to reduce the number of words that will be used for the classification system without losing its semantic coverage, increasing its efficiency. The experiment divided the documents classification process in three phases: a) NP preprocessing b) system training; and c) classification experiments. In the first step, a corpus of digitalized texts was submitted to a natural language processing platform1 in which the part-of-speech tagging was done, and them PERL scripts pertaining to the PALAVRAS package were used to extract the Noun Phrases. The preprocessing also involved the tasks of a) removing NP low meaning pre-modifiers, as quantifiers; b) identification of synonyms and corresponding substitution for common hyperonyms; and c) stemming of the relevant words contained in the NP, for similitude checking with other NPs. The first tests with the resulting documents have demonstrated its effectiveness. We have compared the structural similarity of the documents before and after the whole pre-processing steps of phase one. The texts maintained the consistency with the original and have kept the readability. The second phase involves submitting the modified documents to a SVM algorithm to identify clusters and classify the documents. The classification rules are to be established using a machine learning approach. Finally, tests will be conducted to check the effectiveness of the whole process.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  7. Losee, R.M.: ¬A Gray code based ordering for documents on shelves : classification for browsing and retrieval (1992) 0.03 
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    Abstract
    A document classifier places documents together in a linear arrangement for browsing or high-speed access by human or computerised information retrieval systems. Requirements for document classification and browsing systems are developed from similarity measures, distance measures, and the notion of subject aboutness. A requirement that documents be arranged in decreasing order of similarity as the distance from a given document increases can often not be met. Based on these requirements, information-theoretic considerations, and the Gray code, a classification system is proposed that can classifiy documents without human intervention. A measure of classifier performance is developed, and used to evaluate experimental results comparing the distance between subject headings assigned to documents given classifications from the proposed system and the Library of Congress Classification (LCC) system
  8. Suominen, O.; Koskenniemi, I.: Annif Analyzer Shootout : comparing text lemmatization methods for automated subject indexing (2022) 0.03 
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    Abstract
    Automated text classification is an important function for many AI systems relevant to libraries, including automated subject indexing and classification. When implemented using the traditional natural language processing (NLP) paradigm, one key part of the process is the normalization of words using stemming or lemmatization, which reduces the amount of linguistic variation and often improves the quality of classification. In this paper, we compare the output of seven different text lemmatization algorithms as well as two baseline methods. We measure how the choice of method affects the quality of text classification using example corpora in three languages. The experiments have been performed using the open source Annif toolkit for automated subject indexing and classification, but should generalize also to other NLP toolkits and similar text classification tasks. The results show that lemmatization methods in most cases outperform baseline methods in text classification particularly for Finnish and Swedish text, but not English, where baseline methods are most effective. The differences between lemmatization methods are quite small. The systematic comparison will help optimize text classification pipelines and inform the further development of the Annif toolkit to incorporate a wider choice of normalization methods.
  9. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.03 
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    Abstract
    This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
    Source
    Cataloging and classification quarterly. 57(2019) no.5, S.315-336
  10. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.03 
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    Abstract
    In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
    Source
    Cataloging and classification quarterly. 60(2022) no.8, p.807-835
  11. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.03 
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    Abstract
    Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. It is also desirable to be able to identify a small set of entities (e.g., authors, citations, bibliographic records) which are most relevant to a query. This gets more difficult when the amount of data increases dramatically. Data sparsity and model scalability are the major challenges to solving this type of extreme multilabel classification problem automatically. In this paper, we propose to address this problem in two steps: we first embed different types of entities into the same semantic space, where similarity could be computed easily; second, we propose a novel non-parametric method to identify the most relevant entities in addition to direct semantic similarities. We show how effectively this approach predicts even very specialised subjects, which are associated with few documents in the training set and are more problematic for a classifier.
    Footnote
    Beitrag eines Special Issue: Research Information Systems and Science Classifications; including papers from "Trajectories for Research: Fathoming the Promise of the NARCIS Classification," 27-28 September 2018, The Hague, The Netherlands.
  12. Toepfer, M.; Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints 0.03 
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    Abstract
    Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.
  13. Keller, A.: Attitudes among German- and English-speaking librarians toward (automatic) subject indexing (2015) 0.02 
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    Abstract
    The survey described in this article investigates the attitudes of librarians in German- and English-speaking countries toward subject indexing in general, and automatic subject indexing in particular. The results show great similarity between attitudes in both language areas. Respondents agree that the current quality standards should be upheld and dismiss critical voices claiming that subject indexing has lost relevance. With regard to automatic subject indexing, respondents demonstrate considerable skepticism-both with regard to the likely timeframe and the expected quality of such systems. The author considers how this low acceptance poses a difficulty for those involved in change management.
    Source
    Cataloging and classification quarterly. 53(2015) no.8, S.895-904
  14. Golub, K.: Automatic subject indexing of text (2019) 0.02 
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    Abstract
    Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collec-tions, and enhance consistency of the metadata. In this work, au-tomatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are dis-cussed, in terms of their similarities and differences, advantages and disadvantages for automatic assigned indexing from KOSs: "text categorization," "document clustering," and "document classification." Text categorization is perhaps the most wide-spread, machine-learning approach with what seems generally good reported performance. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. Document classification re-uses the intellectual effort invested into creating a KOS for sub-ject indexing and even simple string-matching algorithms have been reported to achieve good results, because one concept can be described using a number of different terms, including equiv-alent, related, narrower and broader terms. Finally, applicability of automatic subject indexing to operative information systems and challenges of evaluation are outlined, suggesting the need for more research.
  15. Asula, M.; Makke, J.; Freienthal, L.; Kuulmets, H.-A.; Sirel, R.: Kratt: developing an automatic subject indexing tool for the National Library of Estonia : how to transfer metadata information among work cluster members (2021) 0.02 
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    Abstract
    Manual subject indexing in libraries is a time-consuming and costly process and the quality of the assigned subjects is affected by the cataloger's knowledge on the specific topics contained in the book. Trying to solve these issues, we exploited the opportunities arising from artificial intelligence to develop Kratt: a prototype of an automatic subject indexing tool. Kratt is able to subject index a book independent of its extent and genre with a set of keywords present in the Estonian Subject Thesaurus. It takes Kratt approximately one minute to subject index a book, outperforming humans 10-15 times. Although the resulting keywords were not considered satisfactory by the catalogers, the ratings of a small sample of regular library users showed more promise. We also argue that the results can be enhanced by including a bigger corpus for training the model and applying more careful preprocessing techniques.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.775-793
  16. Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012) 0.02 
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    Abstract
    The German subject headings authority file (Schlagwortnormdatei/SWD) provides a broad controlled vocabulary for indexing documents of all subjects. Traditionally used for intellectual subject cataloguing primarily of books the Deutsche Nationalbibliothek (DNB, German National Library) has been working on developping and implementing procedures for automated assignment of subject headings for online publications. This project, its results and problems are sketched in the paper.
    Content
    Beitrag für die Tagung: Beyond libraries - subject metadata in the digital environment and semantic web. IFLA Satellite Post-Conference, 17-18 August 2012, Tallinn. Vgl.: http://http://www.nlib.ee/index.php?id=17763.
    Source
    Cataloguing & Classification Quarterly 52(2014) no.1, S.102-109
  17. Oliver, C.: Leveraging KOS to extend our reach with automated processes (2021) 0.02 
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    Abstract
    This article provides a conclusion to the special issue on Artificial Intelligence (AI) and Automated Processes for Subject Access. The authors who contributed to this special issue have provoked interesting questions as well as bringing attention to important issues. This concluding article looks at common themes and highlights some of the questions raised.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.868-874
  18. Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2014) 0.02 
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    Abstract
    The German Integrated Authority File (Gemeinsame Normdatei, GND), provides a broad controlled vocabulary for indexing documents on all subjects. Traditionally used for intellectual subject cataloging primarily for books, the Deutsche Nationalbibliothek (DNB, German National Library) has been working on developing and implementing procedures for automated assignment of subject headings for online publications. This project, its results, and problems are outlined in this article.
    Footnote
    Contribution in a special issue "Beyond libraries: Subject metadata in the digital environment and Semantic Web" - Enthält Beiträge der gleichnamigen IFLA Satellite Post-Conference, 17-18 August 2012, Tallinn.
    Source
    Cataloging and classification quarterly. 52(2014) no.1, S.102-109
  19. Moulaison-Sandy, H.; Adkins, D.; Bossaller, J.; Cho, H.: ¬An automated approach to describing fiction : a methodology to use book reviews to identify affect (2021) 0.02 
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    Abstract
    Subject headings and genre terms are notoriously difficult to apply, yet are important for fiction. The current project functions as a proof of concept, using a text-mining methodology to identify affective information (emotion and tone) about fiction titles from professional book reviews as a potential first step in automating the subject analysis process. Findings are presented and discussed, comparing results to the range of aboutness and isness information in library cataloging records. The methodology is likewise presented, and how future work might expand on the current project to enhance catalog records through text-mining is explored.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.794-814
  20. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.02 
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    Abstract
    This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.815-834

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