Search (42 results, page 2 of 3)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2010 TO 2020}
  1. 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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    Date
    20. 1.2015 18:30:22
    Footnote
    Beitrag in einem Special Issue: Information Science in the German-speaking Countries.
    Source
    Aslib journal of information management. 71(2019) no.3, S.415-439
  2. Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018) 0.00 
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  3. Karpathy, A.; Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions (2015) 0.00 
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    Abstract
    We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.
  4. Gil-Leiva, I.: SISA-automatic indexing system for scientific articles : experiments with location heuristics rules versus TF-IDF rules (2017) 0.00 
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    Abstract
    Indexing is contextualized and a brief description is provided of some of the most used automatic indexing systems. We describe SISA, a system which uses location heuristics rules, statistical rules like term frequency (TF) or TF-IDF to obtain automatic or semi-automatic indexing, depending on the user's preference. The aim of this research is to ascertain which rules (location heuristics rules or TF-IDF rules) provide the best indexing terms. SISA is used to obtain the automatic indexing of 200 scientific articles on fruit growing written in Portuguese. It uses, on the one hand, location heuristics rules founded on the value of certain parts of the articles for indexing such as titles, abstracts, keywords, headings, first paragraph, conclusions and references and, on the other, TF-IDF rules. The indexing is then evaluated to ascertain retrieval performance through recall, precision and f-measure. Automatic indexing of the articles with location heuristics rules provided the best results with the evaluation measures.
  5. Donahue, J.; Hendricks, L.A.; Guadarrama, S.; Rohrbach, M.; Venugopalan, S.; Saenko, K.; Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description (2014) 0.00 
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    Abstract
    Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
  6. 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
  7. 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
  8. Vlachidis, A.; Tudhope, D.: ¬A knowledge-based approach to information extraction for semantic interoperability in the archaeology domain (2016) 0.00 
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    Abstract
    The article presents a method for automatic semantic indexing of archaeological grey-literature reports using empirical (rule-based) Information Extraction techniques in combination with domain-specific knowledge organization systems. The semantic annotation system (OPTIMA) performs the tasks of Named Entity Recognition, Relation Extraction, Negation Detection, and Word-Sense Disambiguation using hand-crafted rules and terminological resources for associating contextual abstractions with classes of the standard ontology CIDOC Conceptual Reference Model (CRM) for cultural heritage and its archaeological extension, CRM-EH. Relation Extraction (RE) performance benefits from a syntactic-based definition of RE patterns derived from domain oriented corpus analysis. The evaluation also shows clear benefit in the use of assistive natural language processing (NLP) modules relating to Word-Sense Disambiguation, Negation Detection, and Noun Phrase Validation, together with controlled thesaurus expansion. The semantic indexing results demonstrate the capacity of rule-based Information Extraction techniques to deliver interoperable semantic abstractions (semantic annotations) with respect to the CIDOC CRM and archaeological thesauri. Major contributions include recognition of relevant entities using shallow parsing NLP techniques driven by a complimentary use of ontological and terminological domain resources and empirical derivation of context-driven RE rules for the recognition of semantic relationships from phrases of unstructured text.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1138-1152
  9. 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.
    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.1, S.121-133
  10. 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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    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
  11. 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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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.835-849
  12. Vilares, D.; Alonso, M.A.; Gómez-Rodríguez, C.: On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages (2015) 0.00 
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    Abstract
    Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product, or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focused on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic, and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1799-1816
  13. 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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  14. Cui, H.; Boufford, D.; Selden, P.: Semantic annotation of biosystematics literature without training examples (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.522-542
  15. Kajanan, S.; Bao, Y.; Datta, A.; VanderMeer, D.; Dutta, K.: Efficient automatic search query formulation using phrase-level analysis (2014) 0.00 
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    Abstract
    Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1058-1075
  16. 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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    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
  17. 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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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.1, S.83-105
  18. Blank, I.; Rokach, L.; Shani, G.: Leveraging metadata to recommend keywords for academic papers (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3073-3091
  19. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.00 
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    Information processing and management. 54(2018) no.6, S.1345-1358
  20. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.00 
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    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.

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