Search (59 results, page 2 of 3)

  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2000 TO 2010}
  1. Ahlgren, P.; Kekäläinen, J.: Indexing strategies for Swedish full text retrieval under different user scenarios (2007) 0.01 
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    Abstract
    This paper deals with Swedish full text retrieval and the problem of morphological variation of query terms in the document database. The effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Further, two of these three combinations used indexing strategies that employed compound splitting. Normalization and compound splitting were performed by SWETWOL, a morphological analyzer for the Swedish language. A fourth combination attempted to group related terms by right hand truncation of query terms. The four combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. The five combinations were evaluated under six different user scenarios, where each scenario simulated a certain user type. The four alternative combinations outperformed the baseline, for each user scenario. The truncation combination had the best performance under each user scenario. The main conclusion of the paper is that normalization and right hand truncation (performed by a search expert) enhanced retrieval effectiveness in comparison to the baseline. The performance of the three combinations of indexing strategies with query terms based on normalization was not far below the performance of the truncation combination.
    Source
    Information processing and management. 43(2007) no.1, S.81-102
  2. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.00 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  3. Mongin, L.; Fu, Y.Y.; Mostafa, J.: Open Archives data Service prototype and automated subject indexing using D-Lib archive content as a testbed (2003) 0.00 
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    Abstract
    The Indiana University School of Library and Information Science opened a new research laboratory in January 2003; The Indiana University School of Library and Information Science Information Processing Laboratory [IU IP Lab]. The purpose of the new laboratory is to facilitate collaboration between scientists in the department in the areas of information retrieval (IR) and information visualization (IV) research. The lab has several areas of focus. These include grid and cluster computing, and a standard Java-based software platform to support plug and play research datasets, a selection of standard IR modules and standard IV algorithms. Future development includes software to enable researchers to contribute datasets, IR algorithms, and visualization algorithms into the standard environment. We decided early on to use OAI-PMH as a resource discovery tool because it is consistent with our mission.
  4. Ladewig, C.; Henkes, M.: Verfahren zur automatischen inhaltlichen Erschließung von elektronischen Texten : ASPECTIX (2001) 0.00 
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    Abstract
    Das Verfahren zur automatischen syntaktischen inhaltlichen Erschließung von elektronischen Texten, AspectiX, basiert auf einem Index, dessen Elemente mit einer universellen Aspekt-Klassifikation verknüpft sind, die es erlauben, ein syntaktisches Retrieval durchzuführen. Mit diesen, auf den jeweiligen Suchgegenstand inhaltlich bezogenen Klassifikationselementen, werden die Informationen in elektronischen Texten mit bekannten Suchalgorithmen abgefragt und die Ergebnisse entsprechend der Aspektverknüpfung ausgewertet. Mit diesen Aspekten ist es möglich, unbekannte Textdokumente automatisch fachgebiets- und sprachunabhängig nach Inhalten zu klassifizieren und beim Suchen in einem Textcorpus nicht nur auf die Verwendung von Zeichenfolgen angewiesen zu sein wie bei Suchmaschinen im WWW. Der Index kann bei diesen Vorgängen intellektuell und automatisch weiter ausgebaut werden und liefert Ergebnisse im Retrieval von nahezu 100 Prozent Precision, bei gleichzeitig nahezu 100 Prozent Recall. Damit ist das Verfahren AspectiX allen anderen Recherchetools um bis zu 40 Prozent an Precision bzw. Recall überlegen, wie an zahlreichen Recherchen in drei Datenbanken, die unterschiedlich groß und thematisch unähnlich sind, nachgewiesen wird
    Source
    nfd Information - Wissenschaft und Praxis. 52(2001) H.3, S.159-164
  5. Souza, R.R.; Raghavan, K.S.: ¬A methodology for noun phrase-based automatic indexing (2006) 0.00 
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    Abstract
    The scholarly community is increasingly employing the Web both for publication of scholarly output and for locating and accessing relevant scholarly literature. Organization of this vast body of digital information assumes significance in this context. The sheer volume of digital information to be handled makes traditional indexing and knowledge representation strategies ineffective and impractical. It is, therefore, worth exploring new approaches. An approach being discussed considers the intrinsic semantics of texts of documents. Based on the hypothesis that noun phrases in a text are semantically rich in terms of their ability to represent the subject content of the document, this approach seeks to identify and extract noun phrases instead of single keywords, and use them as descriptors. This paper presents a methodology that has been developed for extracting noun phrases from Portuguese texts. The results of an experiment carried out to test the adequacy of the methodology are also presented.
  6. Mansour, N.; Haraty, R.A.; Daher, W.; Houri, M.: ¬An auto-indexing method for Arabic text (2008) 0.00 
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    Abstract
    This work addresses the information retrieval problem of auto-indexing Arabic documents. Auto-indexing a text document refers to automatically extracting words that are suitable for building an index for the document. In this paper, we propose an auto-indexing method for Arabic text documents. This method is mainly based on morphological analysis and on a technique for assigning weights to words. The morphological analysis uses a number of grammatical rules to extract stem words that become candidate index words. The weight assignment technique computes weights for these words relative to the container document. The weight is based on how spread is the word in a document and not only on its rate of occurrence. The candidate index words are then sorted in descending order by weight so that information retrievers can select the more important index words. We empirically verify the usefulness of our method using several examples. For these examples, we obtained an average recall of 46% and an average precision of 64%.
    Source
    Information processing and management. 44(2008) no.4, S.1538-1545
  7. Dolamic, L.; Savoy, J.: Indexing and searching strategies for the Russian language (2009) 0.00 
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    Abstract
    This paper describes and evaluates various stemming and indexing strategies for the Russian language. We design and evaluate two stemming approaches, a light and a more aggressive one, and compare these stemmers to the Snowball stemmer, to no stemming, and also to a language-independent approach (n-gram). To evaluate the suggested stemming strategies we apply various probabilistic information retrieval (IR) models, including the Okapi, the Divergence from Randomness (DFR), a statistical language model (LM), as well as two vector-space approaches, namely, the classical tf idf scheme and the dtu-dtn model. We find that the vector-space dtu-dtn and the DFR models tend to result in better retrieval effectiveness than the Okapi, LM, or tf idf models, while only the latter two IR approaches result in statistically significant performance differences. Ignoring stemming generally reduces the MAP by more than 50%, and these differences are always significant. When applying an n-gram approach, performance differences are usually lower than an approach involving stemming. Finally, our light stemmer tends to perform best, although performance differences between the light, aggressive, and Snowball stemmers are not statistically significant.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2540-2547
  8. Nohr, H.: Theorie des Information Retrieval II : Automatische Indexierung (2004) 0.00 
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    Abstract
    Ein großer Teil der Informationen - Schätzungen zufolge bis zu 80% - liegt in Organisationen in unstrukturierten Dokumenten vor. In der Vergangenheit wurden Lösungen für das Management strukturierter Informationen entwickelt, die es nun auch zu erreichen gilt für unstrukturierte Informationen. Neben Verfahren des Data Mining für die Datenanalyse treten Versuche, Text Mining (Lit. 06) auf die Textanalyse anzuwenden. Um gezielt Dokumente im Repository suchen zu können, ist eine effektive Inhaltserkennung und -kennzeichnung erforderlich, d.h. eine Zuordnung der Dokumente zu Themengebieten bzw die Speicherung geeigneter Indexterme als Metadaten. Zu diesem Zweck müssen die Dokumenteninhalte repräsentiert, d.h. indexiert oder klassifiziert, werden. Dokumentanalyse dient auch der Steuerung des Informations- und Dokumentenflusses. Ziel ist die Einleitung eines "Workflow nach Posteingang". Eine Dokumentanalyse kann anhand erkannter Merkmale Eingangspost automatisch an den Sachbearbeiter oder die zuständige Organisationseinheit (Rechnungen in die Buchhaltung, Aufträge in den Vertrieb) im Unternehmen leiten. Dokumentanalysen werden auch benötigt, wenn Mitarbeiter über einen persönlichen Informationsfilter relevante Dokumente automatisch zugestellt bekommen sollen. Aufgrund der Systemintegration werden Indexierungslösungen in den Funktionsumfang von DMS- bzw. Workflow-Produkten integriert. Eine Architektur solcher Systeme zeigt Abb. 1. Die Architektur zeigt die Indexierungs- bzw. Klassifizierungsfunktion im Zentrum der Anwendung. Dabei erfüllt sie Aufgaben für die Repräsentation von Dokumenten (Metadaten) und das spätere Retrieval.
    Source
    Grundlagen der praktischen Information und Dokumentation. 5., völlig neu gefaßte Ausgabe. 2 Bde. Hrsg. von R. Kuhlen, Th. Seeger u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried. Bd.1: Handbuch zur Einführung in die Informationswissenschaft und -praxis
  9. Snajder, J.; Dalbelo Basic, B.D.; Tadic, M.: Automatic acquisition of inflectional lexica for morphological normalisation (2008) 0.00 
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    Abstract
    Due to natural language morphology, words can take on various morphological forms. Morphological normalisation - often used in information retrieval and text mining systems - conflates morphological variants of a word to a single representative form. In this paper, we describe an approach to lexicon-based inflectional normalisation. This approach is in between stemming and lemmatisation, and is suitable for morphological normalisation of inflectionally complex languages. To eliminate the immense effort required to compile the lexicon by hand, we focus on the problem of acquiring automatically an inflectional morphological lexicon from raw corpora. We propose a convenient and highly expressive morphology representation formalism on which the acquisition procedure is based. Our approach is applied to the morphologically complex Croatian language, but it should be equally applicable to other languages of similar morphological complexity. Experimental results show that our approach can be used to acquire a lexicon whose linguistic quality allows for rather good normalisation performance.
    Source
    Information processing and management. 44(2008) no.5, S.1720-1731
  10. Tsai, C.-F.; McGarry, K.; Tait, J.: Qualitative evaluation of automatic assignment of keywords to images (2006) 0.00 
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    Abstract
    In image retrieval, most systems lack user-centred evaluation since they are assessed by some chosen ground truth dataset. The results reported through precision and recall assessed against the ground truth are thought of as being an acceptable surrogate for the judgment of real users. Much current research focuses on automatically assigning keywords to images for enhancing retrieval effectiveness. However, evaluation methods are usually based on system-level assessment, e.g. classification accuracy based on some chosen ground truth dataset. In this paper, we present a qualitative evaluation methodology for automatic image indexing systems. The automatic indexing task is formulated as one of image annotation, or automatic metadata generation for images. The evaluation is composed of two individual methods. First, the automatic indexing annotation results are assessed by human subjects. Second, the subjects are asked to annotate some chosen images as the test set whose annotations are used as ground truth. Then, the system is tested by the test set whose annotation results are judged against the ground truth. Only one of these methods is reported for most systems on which user-centred evaluation are conducted. We believe that both methods need to be considered for full evaluation. We also provide an example evaluation of our system based on this methodology. According to this study, our proposed evaluation methodology is able to provide deeper understanding of the system's performance.
    Source
    Information processing and management. 42(2006) no.1, S.136-154
  11. Lohmann, H.: KASCADE: Dokumentanreicherung und automatische Inhaltserschließung : Projektbericht und Ergebnisse des Retrievaltests (2000) 0.00 
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    Abstract
    Verbesserungen des Gewichtungsverfahrens sollten schließlich unterstützt werden durch Maßnahmen zur Optimierung der Retrievalumgebung, etwa durch - den Einsatz von Hypertextwerkzeugen; - die Integration der THEAS-Komponente, mit der die automatische Mehrwortgruppengewinnung mit Hilfe eines Mehrwortgruppen-Parsers möglich ist; dies könnte im Rahmen der Dialogführung mit dem Nutzer eingesetzt werden, wenn dieser Teilkomponenten solcher Mehrwortgruppen im Retrieval verwendet. Mit THEAS wäre daneben der Einstieg in das Retrieval über das Register mit kanonischen Formen möglich; - die Integration von Wörterbuchfunktionen zur Benutzerunterstützung. Eine Weiterentwicklung des SELIX-Verfahrens könnte daneben in diese Richtungen erfolgen: - Bildung von Dokument-Clustern. Dabei werden Dokumente in einem Dokumenten-Raum einander in dem Maße zugeordnet, in dem ihre selektierten Grundformen übereinstimmen. - Errichtung von statistisch basierten semantischen Netzen, in denen Grundformen einander in einem Begriffs-Raum in dem Maße zugeordnet werden, in dem sie "gemeinsam" in Dokumenten auftreten.
    RSWK
    Online-Katalog / Automatische Indexierung / Inhaltsverzeichnis / Scanning / Information Retrieval / Projekt
    Subject
    Online-Katalog / Automatische Indexierung / Inhaltsverzeichnis / Scanning / Information Retrieval / Projekt
  12. Chung, Y.M.; Lee, J.Y.: ¬A corpus-based approach to comparative evaluation of statistical term association measures (2001) 0.00 
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    Abstract
    Statistical association measures have been widely applied in information retrieval research, usually employing a clustering of documents or terms on the basis of their relationships. Applications of the association measures for term clustering include automatic thesaurus construction and query expansion. This research evaluates the similarity of six association measures by comparing the relationship and behavior they demonstrate in various analyses of a test corpus. Analysis techniques include comparisons of highly ranked term pairs and term clusters, analyses of the correlation among the association measures using Pearson's correlation coefficient and MDS mapping, and an analysis of the impact of a term frequency on the association values by means of z-score. The major findings of the study are as follows: First, the most similar association measures are mutual information and Yule's coefficient of colligation Y, whereas cosine and Jaccard coefficients, as well as X**2 statistic and likelihood ratio, demonstrate quite similar behavior for terms with high frequency. Second, among all the measures, the X**2 statistic is the least affected by the frequency of terms. Third, although cosine and Jaccard coefficients tend to emphasize high frequency terms, mutual information and Yule's Y seem to overestimate rare terms
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.4, S.283-296
  13. Roberts, D.; Souter, C.: ¬The automation of controlled vocabulary subject indexing of medical journal articles (2000) 0.00 
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    Abstract
    This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of 'concept strings' from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules.
  14. Humphrey, S.M.; Névéol, A.; Browne, A.; Gobeil, J.; Ruch, P.; Darmoni, S.J.: Comparing a rule-based versus statistical system for automatic categorization of MEDLINE documents according to biomedical specialty (2009) 0.00 
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    Abstract
    Automatic document categorization is an important research problem in Information Science and Natural Language Processing. Many applications, including, Word Sense Disambiguation and Information Retrieval in large collections, can benefit from such categorization. This paper focuses on automatic categorization of documents from the biomedical literature into broad discipline-based categories. Two different systems are described and contrasted: CISMeF, which uses rules based on human indexing of the documents by the Medical Subject Headings (MeSH) controlled vocabulary in order to assign metaterms (MTs), and Journal Descriptor Indexing (JDI), based on human categorization of about 4,000 journals and statistical associations between journal descriptors (JDs) and textwords in the documents. We evaluate and compare the performance of these systems against a gold standard of humanly assigned categories for 100 MEDLINE documents, using six measures selected from trec_eval. The results show that for five of the measures performance is comparable, and for one measure JDI is superior. We conclude that these results favor JDI, given the significantly greater intellectual overhead involved in human indexing and maintaining a rule base for mapping MeSH terms to MTs. We also note a JDI method that associates JDs with MeSH indexing rather than textwords, and it may be worthwhile to investigate whether this JDI method (statistical) and CISMeF (rule-based) might be combined and then evaluated showing they are complementary to one another.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2530-2539
  15. Scherer, B.: Automatische Indexierung und ihre Anwendung im DFG-Projekt "Gemeinsames Portal für Bibliotheken, Archive und Museen (BAM)" (2003) 0.00 
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    Abstract
    Automatische Indexierung verzeichnet schon seit einigen Jahren aufgrund steigender Informationsflut ein wachsendes Interesse. Allerdings gibt es immer noch Vorbehalte gegenüber der intellektuellen Indexierung in Bezug auf Qualität und größerem Aufwand der Systemimplementierung bzw. -pflege. Neuere Entwicklungen aus dem Bereich des Wissensmanagements, wie beispielsweise Verfahren aus der Künstlichen Intelligenz, der Informationsextraktion, dem Text Mining bzw. der automatischen Klassifikation sollen die automatische Indexierung aufwerten und verbessern. Damit soll eine intelligentere und mehr inhaltsbasierte Erschließung geleistet werden. In dieser Masterarbeit wird außerhalb der Darstellung von Grundlagen und Verfahren der automatischen Indexierung sowie neueren Entwicklungen auch Möglichkeiten der Evaluation dargestellt. Die mögliche Anwendung der automatischen Indexierung im DFG-ProjektGemeinsames Portal für Bibliotheken, Archive und Museen (BAM)" bilden den Schwerpunkt der Arbeit. Im Portal steht die bibliothekarische Erschließung von Texten im Vordergrund. In einem umfangreichen Test werden drei deutsche, linguistische Systeme mit statistischen Verfahren kombiniert (die aber teilweise im System bereits integriert ist) und evaluiert, allerdings nur auf der Basis der ausgegebenen Indexate. Abschließend kann festgestellt werden, dass die Ergebnisse und damit die Qualität (bezogen auf die Indexate) von intellektueller und automatischer Indexierung noch signifikant unterschiedlich sind. Die Gründe liegen in noch zu lösenden semantischen Problemen bzw, in der Obereinstimmung mit Worten aus einem Thesaurus, die von einem automatischen Indexierungssystem nicht immer nachvollzogen werden kann. Eine Inhaltsanreicherung mit den Indexaten zum Vorteil beim Retrieval kann, je nach System oder auch über die Einbindung durch einen Thesaurus, erreicht werden.
    Footnote
    Masterarbeit im Studiengang Information Engineering zur Erlagung des Grades eines Master of Science in Information science,
  16. Grummann, M.: Sind Verfahren zur maschinellen Indexierung für Literaturbestände Öffentlicher Bibliotheken geeignet? : Retrievaltests von indexierten ekz-Daten mit der Software IDX (2000) 0.00 
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    Source
    Bibliothek: Forschung und Praxis. 24(2000) H.3, S.297-318
  17. Jones, S.; Paynter, G.W.: Automatic extractionof document keyphrases for use in digital libraries : evaluations and applications (2002) 0.00 
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    Abstract
    This article describes an evaluation of the Kea automatic keyphrase extraction algorithm. Document keyphrases are conventionally used as concise descriptors of document content, and are increasingly used in novel ways, including document clustering, searching and browsing interfaces, and retrieval engines. However, it is costly and time consuming to manually assign keyphrases to documents, motivating the development of tools that automatically perform this function. Previous studies have evaluated Kea's performance by measuring its ability to identify author keywords and keyphrases, but this methodology has a number of well-known limitations. The results presented in this article are based on evaluations by human assessors of the quality and appropriateness of Kea keyphrases. The results indicate that, in general, Kea produces keyphrases that are rated positively by human assessors. However, typical Kea settings can degrade performance, particularly those relating to keyphrase length and domain specificity. We found that for some settings, Kea's performance is better than that of similar systems, and that Kea's ranking of extracted keyphrases is effective. We also determined that author-specified keyphrases appear to exhibit an inherent ranking, and that they are rated highly and therefore suitable for use in training and evaluation of automatic keyphrasing systems.
    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.8, S.653-677
  18. Witschel, H.F.: Terminology extraction and automatic indexing : comparison and qualitative evaluation of methods (2005) 0.00 
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    Abstract
    Many terminology engineering processes involve the task of automatic terminology extraction: before the terminology of a given domain can be modelled, organised or standardised, important concepts (or terms) of this domain have to be identified and fed into terminological databases. These serve in further steps as a starting point for compiling dictionaries, thesauri or maybe even terminological ontologies for the domain. For the extraction of the initial concepts, extraction methods are needed that operate on specialised language texts. On the other hand, many machine learning or information retrieval applications require automatic indexing techniques. In Machine Learning applications concerned with the automatic clustering or classification of texts, often feature vectors are needed that describe the contents of a given text briefly but meaningfully. These feature vectors typically consist of a fairly small set of index terms together with weights indicating their importance. Short but meaningful descriptions of document contents as provided by good index terms are also useful to humans: some knowledge management applications (e.g. topic maps) use them as a set of basic concepts (topics). The author believes that the tasks of terminology extraction and automatic indexing have much in common and can thus benefit from the same set of basic algorithms. It is the goal of this paper to outline some methods that may be used in both contexts, but also to find the discriminating factors between the two tasks that call for the variation of parameters or application of different techniques. The discussion of these methods will be based on statistical, syntactical and especially morphological properties of (index) terms. The paper is concluded by the presentation of some qualitative and quantitative results comparing statistical and morphological methods.
  19. Newman, D.J.; Block, S.: Probabilistic topic decomposition of an eighteenth-century American newspaper (2006) 0.00 
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    Abstract
    We use a probabilistic mixture decomposition method to determine topics in the Pennsylvania Gazette, a major colonial U.S. newspaper from 1728-1800. We assess the value of several topic decomposition techniques for historical research and compare the accuracy and efficacy of various methods. After determining the topics covered by the 80,000 articles and advertisements in the entire 18th century run of the Gazette, we calculate how the prevalence of those topics changed over time, and give historically relevant examples of our findings. This approach reveals important information about the content of this colonial newspaper, and suggests the value of such approaches to a more complete understanding of early American print culture and society.
    Date
    22. 7.2006 17:32:00
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.753-767
  20. Goller, C.; Löning, J.; Will, T.; Wolff, W.: Automatic document classification : a thourough evaluation of various methods (2000) 0.00 
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    Abstract
    (Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods

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