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  • × language_ss:"e"
  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2010 TO 2020}
  1. Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018) 0.01 
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    Abstract
    Seit Herbst 2017 findet in der Deutschen Nationalbibliothek die Inhaltserschließung bestimmter Medienwerke rein maschinell statt. Die Qualität dieses Verfahrens, das die Prozessorganisation von Bibliotheken maßgeblich prägen kann, wird unter Fachleuten kontrovers diskutiert. Ihre Standpunkte werden zunächst hinreichend erläutert, ehe die Notwendigkeit einer Qualitätsprüfung des Verfahrens und dessen Grundlagen dargelegt werden. Zentraler Bestandteil einer künftigen Prüfung ist eine Testkollektion. Ihre Erstellung und deren Dokumentation steht im Fokus dieser Arbeit. In diesem Zusammenhang werden auch die Entstehungsgeschichte und Anforderungen an gelungene Testkollektionen behandelt. Abschließend wird ein Retrievaltest durchgeführt, der die Einsatzfähigkeit der erarbeiteten Testkollektion belegt. Seine Ergebnisse dienen ausschließlich der Funktionsüberprüfung. Eine Qualitätsbeurteilung maschineller Inhaltserschließung im Speziellen sowie im Allgemeinen findet nicht statt und ist nicht Ziel der Ausarbeitung.
    Content
    Bachelorarbeit, Bibliothekswissenschaften, Fakultät für Informations- und Kommunikationswissenschaften, Technische Hochschule Köln
    Imprint
    Köln : Technische Hochschule, Fakultät für Informations- und Kommunikationswissenschaften
  2. Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017) 0.01 
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    Abstract
    Die Arbeit analysiert die dynamische Entwicklung und den Gebrauch von Thesaurusbegriffen. Zusätzlich konzentriert sie sich auf die Faktoren, die die Zahl von Indexbegriffen pro Dokument oder Zeitschrift beeinflussen. Als Untersuchungsobjekt dienten der MeSH und die entsprechende Datenbank "MEDLINE". Die wichtigsten Konsequenzen sind: 1. Der MeSH-Thesaurus hat sich durch drei unterschiedliche Phasen jeweils logarithmisch entwickelt. Solch einen Thesaurus sollte folgenden Gleichung folgen: "T = 3.076,6 Ln (d) - 22.695 + 0,0039d" (T = Begriffe, Ln = natürlicher Logarithmus und d = Dokumente). Um solch einen Thesaurus zu konstruieren, muss man demnach etwa 1.600 Dokumente von unterschiedlichen Themen des Bereiches des Thesaurus haben. Die dynamische Entwicklung von Thesauri wie MeSH erfordert die Einführung eines neuen Begriffs pro Indexierung von 256 neuen Dokumenten. 2. Die Verteilung der Thesaurusbegriffe erbrachte drei Kategorien: starke, normale und selten verwendete Headings. Die letzte Gruppe ist in einer Testphase, während in der ersten und zweiten Kategorie die neu hinzukommenden Deskriptoren zu einem Thesauruswachstum führen. 3. Es gibt ein logarithmisches Verhältnis zwischen der Zahl von Index-Begriffen pro Aufsatz und dessen Seitenzahl für die Artikeln zwischen einer und einundzwanzig Seiten. 4. Zeitschriftenaufsätze, die in MEDLINE mit Abstracts erscheinen erhalten fast zwei Deskriptoren mehr. 5. Die Findablity der nicht-englisch sprachigen Dokumente in MEDLINE ist geringer als die englische Dokumente. 6. Aufsätze der Zeitschriften mit einem Impact Factor 0 bis fünfzehn erhalten nicht mehr Indexbegriffe als die der anderen von MEDINE erfassten Zeitschriften. 7. In einem Indexierungssystem haben unterschiedliche Zeitschriften mehr oder weniger Gewicht in ihrem Findability. Die Verteilung der Indexbegriffe pro Seite hat gezeigt, dass es bei MEDLINE drei Kategorien der Publikationen gibt. Außerdem gibt es wenige stark bevorzugten Zeitschriften."
    Footnote
    Dissertation, Humboldt-Universität zu Berlin - Institut für Bibliotheks- und Informationswissenschaft.
    Imprint
    Berlin : Humboldt-Universität zu Berlin / Institut für Bibliotheks- und Informationswissenschaft
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  3. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.00 
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    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  4. Siebenkäs, A.; Markscheffel, B.: Conception of a workflow for the semi-automatic construction of a thesaurus for the German printing industry (2015) 0.00 
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    Abstract
    During the BMWI granted project "Print-IT", the need of a thesaurus based uniform and consistent language for the German printing industry became evident. In this paper we introduce a semi-automatic construction approach for such a thesaurus and present a workflow which supports users to generate thesaurus typical information structures from relevant digitalized resources with the help of common IT-tools.
    Source
    Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  5. Mesquita, L.A.P.; Souza, R.R.; Baracho Porto, R.M.A.: Noun phrases in automatic indexing: : a structural analysis of the distribution of relevant terms in doctoral theses (2014) 0.00 
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    Abstract
    The main objective of this research was to analyze whether there was a characteristic distribution behavior of relevant terms over a scientific text that could contribute as a criterion for their process of automatic indexing. The terms considered in this study were only full noun phrases contained in the texts themselves. The texts were considered a total of 98 doctoral theses of the eight areas of knowledge in a same university. Initially, 20 full noun phrases were automatically extracted from each text as candidates to be the most relevant terms, and each author of each text assigned a relevance value 0-6 (not relevant and highly relevant, respectively) for each of the 20 noun phrases sent. Only, 22.1 % of noun phrases were considered not relevant. A relevance values of the terms assigned by the authors were associated with their positions in the text. Each full noun phrases found in the text was considered as a valid linear position. The results that were obtained showed values resulting from this distribution by considering two types of position: linear, with values consolidated into ten equal consecutive parts; and structural, considering parts of the text (such as introduction, development and conclusion). As a result of considerable importance, all areas of knowledge related to the Natural Sciences showed a characteristic behavior in the distribution of relevant terms, as well as all areas of knowledge related to Social Sciences showed the same characteristic behavior of distribution, but distinct from the Natural Sciences. The difference of the distribution behavior between the Natural and Social Sciences can be clearly visualized through graphs. All behaviors, including the general behavior of all areas of knowledge together, were characterized in polynomial equations and can be applied in future as criteria for automatic indexing. Until the present date this work has become inedited of for two reasons: to present a method for characterizing the distribution of relevant terms in a scientific text, and also, through this method, pointing out a quantitative trait difference between the Natural and Social Sciences.
    Series
    Advances in knowledge organization; vol. 14
    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
  6. Greiner-Petter, A.; Schubotz, M.; Cohl, H.S.; Gipp, B.: Semantic preserving bijective mappings for expressions involving special functions between computer algebra systems and document preparation systems (2019) 0.00 
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    Abstract
    Purpose Modern mathematicians and scientists of math-related disciplines often use Document Preparation Systems (DPS) to write and Computer Algebra Systems (CAS) to calculate mathematical expressions. Usually, they translate the expressions manually between DPS and CAS. This process is time-consuming and error-prone. The purpose of this paper is to automate this translation. This paper uses Maple and Mathematica as the CAS, and LaTeX as the DPS. Design/methodology/approach Bruce Miller at the National Institute of Standards and Technology (NIST) developed a collection of special LaTeX macros that create links from mathematical symbols to their definitions in the NIST Digital Library of Mathematical Functions (DLMF). The authors are using these macros to perform rule-based translations between the formulae in the DLMF and CAS. Moreover, the authors develop software to ease the creation of new rules and to discover inconsistencies. Findings The authors created 396 mappings and translated 58.8 percent of DLMF formulae (2,405 expressions) successfully between Maple and DLMF. For a significant percentage, the special function definitions in Maple and the DLMF were different. An atomic symbol in one system maps to a composite expression in the other system. The translator was also successfully used for automatic verification of mathematical online compendia and CAS. The evaluation techniques discovered two errors in the DLMF and one defect in Maple. Originality/value This paper introduces the first translation tool for special functions between LaTeX and CAS. The approach improves error-prone manual translations and can be used to verify mathematical online compendia and CAS.
    Date
    20. 1.2015 18:30:22
    Footnote
    Beitrag in einem Special Issue: Information Science in the German-speaking Countries.
  7. 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) 0.00 
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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.
    Series
    Advances in knowledge organization; vol. 14
    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
  8. Willis, C.; Losee, R.M.: ¬A random walk on an ontology : using thesaurus structure for automatic subject indexing (2013) 0.00 
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    Abstract
    Relationships between terms and features are an essential component of thesauri, ontologies, and a range of controlled vocabularies. In this article, we describe ways to identify important concepts in documents using the relationships in a thesaurus or other vocabulary structures. We introduce a methodology for the analysis and modeling of the indexing process based on a weighted random walk algorithm. The primary goal of this research is the analysis of the contribution of thesaurus structure to the indexing process. The resulting models are evaluated in the context of automatic subject indexing using four collections of documents pre-indexed with 4 different thesauri (AGROVOC [UN Food and Agriculture Organization], high-energy physics taxonomy [HEP], National Agricultural Library Thesaurus [NALT], and medical subject headings [MeSH]). We also introduce a thesaurus-centric matching algorithm intended to improve the quality of candidate concepts. In all cases, the weighted random walk improves automatic indexing performance over matching alone with an increase in average precision (AP) of 9% for HEP, 11% for MeSH, 35% for NALT, and 37% for AGROVOC. The results of the analysis support our hypothesis that subject indexing is in part a browsing process, and that using the vocabulary and its structure in a thesaurus contributes to the indexing process. The amount that the vocabulary structure contributes was found to differ among the 4 thesauri, possibly due to the vocabulary used in the corresponding thesauri and the structural relationships between the terms. Each of the thesauri and the manual indexing associated with it is characterized using the methods developed here.
    Content
    Korrektur einer Referenz in: JASIST 64(2013) no.8, S.1757.
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  9. Benson, A.C.: Image descriptions and their relational expressions : a review of the literature and the issues (2015) 0.00 
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    Abstract
    Purpose - The purpose of this paper is to survey the treatment of relationships, relationship expressions and the ways in which they manifest themselves in image descriptions. Design/methodology/approach - The term "relationship" is construed in the broadest possible way to include spatial relationships ("to the right of"), temporal ("in 1936," "at noon"), meronymic ("part of"), and attributive ("has color," "has dimension"). The intentions of these vaguely delimited categories with image information, image creation, and description in libraries and archives is complex and in need of explanation. Findings - The review brings into question many generally held beliefs about the relationship problem such as the belief that the semantics of relationships are somehow embedded in the relationship term itself and that image search and retrieval solutions can be found through refinement of word-matching systems. Originality/value - This review has no hope of systematically examining all evidence in all disciplines pertaining to this topic. It instead focusses on a general description of a theoretical treatment in Library and Information Science.
  10. Keller, A.: Attitudes among German- and English-speaking librarians toward (automatic) subject indexing (2015) 0.00 
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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.
  11. Karpathy, A.; Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions (2015) 0.00 
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    Content
    Vgl. auch: http://cs.stanford.edu/people/karpathy/cvpr2015.pdf und http://cs.stanford.edu/people/karpathy/deepimagesent/. Vgl. auch: https://news.ycombinator.com/item?id=8621658.
  12. Flores, F.N.; Moreira, V.P.: Assessing the impact of stemming accuracy on information retrieval : a multilingual perspective (2016) 0.00 
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    Abstract
    The quality of stemming algorithms is typically measured in two different ways: (i) how accurately they map the variant forms of a word to the same stem; or (ii) how much improvement they bring to Information Retrieval systems. In this article, we evaluate various stemming algorithms, in four languages, in terms of accuracy and in terms of their aid to Information Retrieval. The aim is to assess whether the most accurate stemmers are also the ones that bring the biggest gain in Information Retrieval. Experiments in English, French, Portuguese, and Spanish show that this is not always the case, as stemmers with higher error rates yield better retrieval quality. As a byproduct, we also identified the most accurate stemmers and the best for Information Retrieval purposes.
  13. Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018) 0.00 
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    Abstract
    Automatic content indexing is one of the innovations that are increasingly changing the way libraries work. In theory, it promises a cataloguing service that would hardly be possible with humans in terms of speed, quantity and maybe quality. The German National Library (DNB) has also recognised this potential and is increasingly relying on the automatic indexing of their catalogue content. The DNB took a major step in this direction in 2017, which was announced in two papers. The announcement was rather restrained, but the content of the papers is all the more explosive for the library community: Since September 2017, the DNB has discontinued the intellectual indexing of series Band H and has switched to an automatic process for these series. The subject indexing of online publications (series O) has been purely automatical since 2010; from September 2017, monographs and periodicals published outside the publishing industry and university publications will no longer be indexed by people. This raises the question: What is the quality of the automatic indexing compared to the manual work or in other words to which degree can the automatic indexing replace people without a signi cant drop in regards to quality?
  14. Husevag, A.-S.R.: Named entities in indexing : a case study of TV subtitles and metadata records (2016) 0.00 
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    Abstract
    This paper explores the possible role of named entities in an automatic index-ing process, based on text in subtitles. This is done by analyzing entity types, name den-sity and name frequencies in subtitles and metadata records from different TV programs. The name density in metadata records is much higher than the name density in subtitles, and named entities with high frequencies in the subtitles are more likely to be mentioned in the metadata records. Personal names, geographical names and names of organizations where the most prominent entity types in both the news subtitles and news metadata, while persons, works and locations are the most prominent in culture programs.
  15. Simões, M. da Graça; Machado, L.M.; Souza, R.R.; Almeida, M.B.; Tavares Lopes, A.: Automatic indexing and ontologies : the consistency of research chronology and authoring in the context of Information Science (2018) 0.00 
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    Footnote
    U.d.T. 'Indexação automática e ontologias: identificação dos contributos convergentes na ciência da informação' in: Ciência da Informação 46(2017) no.1, S.152-162.
    Series
    Advances in knowledge organization; vol.16
    Source
    Challenges and opportunities for knowledge organization in the digital age: proceedings of the Fifteenth International ISKO Conference, 9-11 July 2018, Porto, Portugal / organized by: International Society for Knowledge Organization (ISKO), ISKO Spain and Portugal Chapter, University of Porto - Faculty of Arts and Humanities, Research Centre in Communication, Information and Digital Culture (CIC.digital) - Porto. Eds.: F. Ribeiro u. M.E. Cerveira
  16. Lu, K.; Mao, J.; Li, G.: Toward effective automated weighted subject indexing : a comparison of different approaches in different environments (2018) 0.00 
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    Abstract
    Subject indexing plays an important role in supporting subject access to information resources. Current subject indexing systems do not make adequate distinctions on the importance of assigned subject descriptors. Assigning numeric weights to subject descriptors to distinguish their importance to the documents can strengthen the role of subject metadata. Automated methods are more cost-effective. This study compares different automated weighting methods in different environments. Two evaluation methods were used to assess the performance. Experiments on three datasets in the biomedical domain suggest the performance of different weighting methods depends on whether it is an abstract or full text environment. Mutual information with bag-of-words representation shows the best average performance in the full text environment, while cosine with bag-of-words representation is the best in an abstract environment. The cosine measure has relatively consistent and robust performance. A direct weighting method, IDF (Inverse Document Frequency), can produce quick and reasonable estimates of the weights. Bag-of-words representation generally outperforms the concept-based representation. Further improvement in performance can be obtained by using the learning-to-rank method to integrate different weighting methods. This study follows up Lu and Mao (Journal of the Association for Information Science and Technology, 66, 1776-1784, 2015), in which an automated weighted subject indexing method was proposed and validated. The findings from this study contribute to more effective weighted subject indexing.
    Footnote
    Vgl. das Erratum in JASIST 69(2018) no.7, S.956.
  17. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.00 
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    Abstract
    A very important extension of the traditional domain of knowledge organization (KO) arises from attempts to incorporate techniques devised in the computer science domain for automatic concept extraction and for grouping, categorizing, clustering and otherwise organizing knowledge using mechanical means. Four specific terms have emerged to identify the most prevalent techniques: machine learning, clustering, automatic indexing, and automatic classification. Our study presents three domain analytical case analyses in search of answers. The first case relies on citations located using the ISKO-supported "Knowledge Organization Bibliography." The second case relies on works in both Web of Science and SCOPUS. Case three applies co-word analysis and citation analysis to the contents of the papers in the present special issue. We observe scholars involved in "clustering" and "automatic classification" who share common thematic emphases. But we have found no coherence, no common activity and no social semantics. We have not found a research front, or a common teleology within the KO domain. We also have found a lively group of authors who have succeeded in submitting papers to this special issue, and their work quite interestingly aligns with the case studies we report. There is an emphasis on KO for information retrieval; there is much work on clustering (which involves conceptual points within texts) and automatic classification (which involves semantic groupings at the meta-document level).
    Content
    Beitrag in einem Special Issue "New Trends for Knowledge Organization, Guest Editor: Renato Rocha Souza".
  18. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.00 
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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.
  19. Williams, R.V.: Hans Peter Luhn and Herbert M. Ohlman : their roles in the origins of keyword-in-context/permutation automatic indexing (2010) 0.00 
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    Abstract
    The invention of automatic indexing using a keyword-in-context approach has generally been attributed solely to Hans Peter Luhn of IBM. This article shows that credit for this invention belongs equally to Luhn and Herbert Ohlman of the System Development Corporation. It also traces the origins of title derivative automatic indexing, its development and implementation, and current status.
  20. Martins, E.F.; Belém, F.M.; Almeida, J.M.; Gonçalves, M.A.: On cold start for associative tag recommendation (2016) 0.00 
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    Abstract
    Tag recommendation strategies that exploit term co-occurrence patterns with tags previously assigned to the target object have consistently produced state-of-the-art results. However, such techniques work only for objects with previously assigned tags. Here we focus on tag recommendation for objects with no tags, a variation of the well-known \textit{cold start} problem. We start by evaluating state-of-the-art co-occurrence based methods in cold start. Our results show that the effectiveness of these methods suffers in this situation. Moreover, we show that employing various automatic filtering strategies to generate an initial tag set that enables the use of co-occurrence patterns produces only marginal improvements. We then propose a new approach that exploits both positive and negative user feedback to iteratively select input tags along with a genetic programming strategy to learn the recommendation function. Our experimental results indicate that extending the methods to include user relevance feedback leads to gains in precision of up to 58% over the best baseline in cold start scenarios and gains of up to 43% over the best baseline in objects that contain some initial tags (i.e., no cold start). We also show that our best relevance-feedback-driven strategy performs well even in scenarios that lack user cooperation (i.e., users may refuse to provide feedback) and user reliability (i.e., users may provide the wrong feedback).

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