Search (26 results, page 1 of 2)

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Date

1. 2.2016 18:25:22

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Abstract

In order to enhance the use of Universal Decimal Classification (UDC) numbers in information retrieval, the authors have represented classification with multilingual thesaurus descriptors and implemented this solution in an automated way. The authors illustrate a solution implemented in a BiblioPhil library system. The standard formats used are UNIMARC for subject authority records (i.e. the UDC-based multilingual thesaurus) and MARC XML support for data transfer. The multilingual thesaurus was built according to existing standards, the constituent parts of the classification notations being used as the basis for search terms in the multilingual information retrieval. The verbal equivalents, descriptors and non-descriptors, are used to expand the number of concepts and are given in Romanian, English and French. This approach saves the time of the indexer and provides more user-friendly and easier access to the bibliographic information. The multilingual aspect of the thesaurus enhances information access for a greater number of online users

Date

22. 7.2010 20:40:56

Theme

Klassifikationssysteme im Online-Retrieval

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Date

17. 4.2012 14:25:22

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

Im Zuge zunehmender Globalisierung werden Wissensorganisationssysteme erforderlich, die ein sprachunabhängiges Retrieval ermöglichen, ohne dass dadurch bereits existierende und national bewährte Wissenssysteme obsolet werden. Das durch die Deutsche Forschungsgemeinschaft (DFG) geförderte und von der Deutschen Nationalbibliothek in Kooperation mit der Fachhochschule Köln durchgeführte Projekt CrissCross leistet einen wesentlichen Beitrag zur Schaffung eines solchen Wissensspeichers, indem es die Sachschlagwörter der deutschen Schlagwortnormdatei (SWD) mit Notationen der Dewey-Dezimalklassifikation sowie mit ihren Äquivalenten der Library of Congress Subject Headings (LCSH) und der französischen Schlagwortsprache RAMEAU (Repertoire d'autorité-matière encyclopédique et alphabétique unifié) verknüpft. Ein erweitertes multilinguales und thesaurusbasiertes Recherchevokabular wird erstellt, das für die inhaltliche Suche nach Dokumenten in heterogen erschlossenen Beständen verwendet werden kann. In diesem Artikel wird die Problematik bei der Verknüpfung semantisch heterogener Systeme unter besonderer Berücksichtigung der Unterschiede zwischen der DDC und der SWD skizziert. Die in CrissCross gewählte Methodik bei der Verknüpfung von SWD und DDC wird vorgestellt. Abschließend wird der Nutzen der erstellten Daten für das Retrieval aufgezeigt.

Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

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Abstract

Multilingual controlled vocabularies are rare and often very limited in the choice of languages offered. TIIARA (Taxonomy for Image Indexing and RetrievAl) is a bilingual taxonomy developed for image indexing and retrieval. This controlled vocabulary offers indexers and image searchers innovative and coherent access points for ordinary images. The preliminary steps of the elaboration of the bilingual structure are presented. For its initial development, TIIARA included only two languages, French and English. As a logical follow-up, TIIARA was translated into eight languages-Arabic, Spanish, Brazilian Portuguese, Mandarin Chinese, Italian, German, Hindi and Russian-in order to increase its international scope. This paper briefly describes the different stages of the development of the bilingual structure. The processes used in the translations are subsequently presented, as well as the main difficulties encountered by the translators. Adding more languages in TIIARA constitutes an added value for a controlled vocabulary meant to be used by image searchers, who are often limited by their lack of knowledge of multiple languages.

Source

Knowledge organization. 43(2016) no.1, S.22-34

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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.

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Abstract

This article describes a framework for cross-language information retrieval that efficiently leverages statistical estimation of translation probabilities. The framework provides a unified perspective into which some earlier work on techniques for cross-language information retrieval based on translation probabilities can be cast. Modeling synonymy and filtering translation probabilities using bidirectional evidence are shown to yield a balance between retrieval effectiveness and query-time (or indexing-time) efficiency that seems well suited large-scale applications. Evaluations with six test collections show consistent improvements over strong baselines.

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Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

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Abstract

We are living in a multilingual world and the diversity in languages which are used to interact with information access systems has generated a wide variety of challenges to be addressed by computer and information scientists. The growing amount of non-English information accessible globally and the increased worldwide exposure of enterprises also necessitates the adaptation of Information Retrieval (IR) methods to new, multilingual settings.Peters, Braschler and Clough present a comprehensive description of the technologies involved in designing and developing systems for Multilingual Information Retrieval (MLIR). They provide readers with broad coverage of the various issues involved in creating systems to make accessible digitally stored materials regardless of the language(s) they are written in. Details on Cross-Language Information Retrieval (CLIR) are also covered that help readers to understand how to develop retrieval systems that cross language boundaries. Their work is divided into six chapters and accompanies the reader step-by-step through the various stages involved in building, using and evaluating MLIR systems. The book concludes with some examples of recent applications that utilise MLIR technologies. Some of the techniques described have recently started to appear in commercial search systems, while others have the potential to be part of future incarnations.The book is intended for graduate students, scholars, and practitioners with a basic understanding of classical text retrieval methods. It offers guidelines and information on all aspects that need to be taken into consideration when building MLIR systems, while avoiding too many 'hands-on details' that could rapidly become obsolete. Thus it bridges the gap between the material covered by most of the classical IR textbooks and the novel requirements related to the acquisition and dissemination of information in whatever language it is stored.

Content

Inhalt: 1 Introduction 2 Within-Language Information Retrieval 3 Cross-Language Information Retrieval 4 Interaction and User Interfaces 5 Evaluation for Multilingual Information Retrieval Systems 6 Applications of Multilingual Information Access

RSWK

Information-Retrieval-System / Mehrsprachigkeit / Abfrage / Zugriff

Subject

Information-Retrieval-System / Mehrsprachigkeit / Abfrage / Zugriff

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Abstract

This paper reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The paper discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and /or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the DDC (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.

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Abstract

We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.

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Abstract

Wie Übersetzer sollten Bibliothekare den Dialog zwischen Autoren, die bereits Werke verfasst haben, und zumeist solchen, die an neuen Werken arbeiten, vermitteln. Sie bedienen sich einer so stark reduzierten "Übersetzungssprache", dass der Dialog oft nicht mehr ausreichend gelingt. Seit zehn Jahren erweitern deshalb im deutschen und amerikanischen Bereich Bibliotheken zunehmend den Terminologieraum ihrer Kataloge durch die wichtigsten, originalsprachlichen Fachbegriffe der Autoren. Dadurch ergeben sich in der Recherche "Docking-Stellen" für terminologische Netze, die zur Query-Expansion statt Dokument-Reduktion genutzt werden können. Die sich daraus ergebende Optimierung des Recalls kann im Dialog mit einem modernen Retrieval-System mittels Facettierungstechnik hinsichtlich Precision verfeinert werden, wobei die ursprünglich oft schwer zugängliche Fachterminologie des Bibliothekars dann auch ohne ungeliebtes Vortraining entschlüsselt werden kann.

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Abstract

Purpose - This paper seeks to examine image retrieval within two different contexts: a monolingual context where the language of the query is the same as the indexing language and a multilingual context where the language of the query is different from the indexing language. The study also aims to compare two different approaches for the indexing of ordinary images representing common objects: traditional image indexing with the use of a controlled vocabulary and free image indexing using uncontrolled vocabulary. Design/methodology/approach - This research uses three data collection methods. An analysis of the indexing terms was employed in order to examine the multiplicity of term types assigned to images. A simulation of the retrieval process involving a set of 30 images was performed with 60 participants. The quantification of the retrieval performance of each indexing approach was based on the usability measures, that is, effectiveness, efficiency and satisfaction of the user. Finally, a questionnaire was used to gather information on searcher satisfaction during and after the retrieval process. Findings - The results of this research are twofold. The analysis of indexing terms associated with all the 3,950 images provides a comprehensive description of the characteristics of the four non-combined indexing forms used for the study. Also, the retrieval simulation results offers information about the relative performance of the six indexing forms (combined and non-combined) in terms of their effectiveness, efficiency (temporal and human) and the image searcher's satisfaction. Originality/value - The findings of the study suggest that, in the near future, the information systems could benefit from allowing an increased coexistence of controlled vocabularies and uncontrolled vocabularies, resulting from collaborative image tagging, for example, and giving the users the possibility to dynamically participate in the image-indexing process, in a more user-centred way.

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Date

22. 9.2014 19:01:18

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

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Abstract

This article reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The article discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and/or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the Dewey Decimal Classification [DDC] (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.

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Abstract

As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries.

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Abstract

Wikipedia is characterized by its dense link structure and a large number of articles in different languages, which make it a notable Web corpus for knowledge extraction and mining, in particular for mining the multilingual associations. In this paper, motivated by a psychological theory of word meaning, we propose a graph-based approach to constructing a cross-language association dictionary (CLAD) from Wikipedia, which can be used in a variety of cross-language accessing and processing applications. In order to evaluate the quality of the mined CLAD, and to demonstrate how the mined CLAD can be used in practice, we explore two different applications of the mined CLAD to cross-language information retrieval (CLIR). First, we use the mined CLAD to conduct cross-language query expansion; and, second, we use it to filter out translation candidates with low translation probabilities. Experimental results on a variety of standard CLIR test collections show that the CLIR retrieval performance can be substantially improved with the above two applications of CLAD, which indicates that the mined CLAD is of sound quality.

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Abstract

This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we present a number of features that may influence the MLIR merging process. These features are mainly extracted from three levels: query, document, and translation. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model. To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method. Moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. The experimental results show that our proposed method can significantly improve merging quality on two different types of datasets. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process. This information might provide us more insight and understanding into MLIR merging.

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Abstract

This article explores how best to use lexical and statistical translation evidence together for cross-language information retrieval (CLIR). Lexical translation evidence is assembled from Wikipedia and from a large machine-readable dictionary, statistical translation evidence is drawn from parallel corpora, and evidence from co-occurrence in the document language provides a basis for limiting the adverse effect of translation ambiguity. Coverage statistics for NII Testbeds and Community for Information Access Research (NTCIR) queries confirm that these resources have complementary strengths. Experiments with translation evidence from a small parallel corpus indicate that even rather rough estimates of translation probabilities can yield further improvements over a strong technique for translation weighting based on using Jensen-Shannon divergence as a term-association measure. Finally, a novel approach to posttranslation query expansion using a random walk over the Wikipedia concept link graph is shown to yield further improvements over alternative techniques for posttranslation query expansion. Evaluation results on the NTCIR-5 English-Korean test collection show statistically significant improvements over strong baselines.

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Abstract

The need for promoting Multilingual Information Retrieval (MLIR) and Access (MLIA) has become evident, now more than ever, given the increase of the online information produced daily in languages other than English. This study aims to explore users' information needs when searching for information across languages. Specifically, the method of questionnaire was employed to shed light on the Library and Information Science (LIS) undergraduate students' use of search engines, databases, digital libraries when searching as well as their needs for multilingual access. This study contributes in informing the design of MLIR systems by focusing on the reasons and situations under which users would search and use information in multiple languages.