Search (93 results, page 1 of 5)

  • × theme_ss:"Automatisches Indexieren"
  1. Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.07 
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    Abstract
    Describes an application of Natural Language Processing (NLP) techniques, in HIRMA (Hypertextual Information Retrieval Managed by ARIOSTO), to the problem of document indexing by referring to a system which incorporates natural language processing techniques to determine the subject of the text of documents and to associate them with relevant semantic indexes. Describes briefly the overall system, details of its implementation on a corpus of scientific abstracts related to environmental topics and experimental evidence of the system's behaviour. Analyzes in detail an experiment designed to evaluate the system's retrieval ability in terms of recall and precision
    Source
    International forum on information and documentation. 22(1997) no.1, S.17-28
  2. Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983) 0.06 
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    Abstract
    A study was done to test the effectiveness of retrieval using title word searching. It was based on actual search profiles used in the Mechanized Information Center at Ohio State University, in order ro replicate as closely as possible actual searching conditions. Fewer than 50% of the relevant titles were retrieved by keywords in titles. The low rate of retrieval can be attributes to three sources: titles themselves, user and information specialist ignorance of the subject vocabulary in use, and to general language problems. Across fields it was found that the social sciences had the best retrieval rate, with science having the next best, and arts and humanities the lowest. Ways to enhance and supplement keyword in title searching on the computer and in printed indexes are discussed.
    Date
    14. 3.1996 13:22:21
  3. Chowdhury, G.G.: Natural language processing and information retrieval : pt.1: basic issues; pt.2: major applications (1991) 0.04 
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    Abstract
    Reviews the basic issues and procedures involved in natural language processing of textual material for final use in information retrieval. Covers: natural language processing; natural language understanding; syntactic and semantic analysis; parsing; knowledge bases and knowledge representation
  4. Pritchard-Schoch, T.: Natural language comes of age (1993) 0.04 
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    Abstract
    Discusses natural languages and the natural language implementations of Westlaw's full-text legal documents, Westlaw Is Natural. Natural language is not aritificial intelligence but a hybrid of linguistics, mathematics and statistics. Provides 3 classes of retrieval models. Explains how Westlaw processes an English query. Assesses WIN. Covers WIN enhancements; the natural language features of Congressional Quarterly's Washington Alert using a document for a query; the personal librarian front end search software and Dowquest from Dow Jones news/retrieval. Conmsiders whether natural language encourages fuzzy thinking and whether Boolean logic will still be needed
  5. Wolfram Language erkennt Bilder (2015) 0.04 
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    Abstract
    Wolfram Research hat seine Cloud-basierte Programmiersprache Wolfram Language um eine Funktion zur Bilderkennung erweitert. Der Hersteller des Computeralgebrasystems Mathematica und Betreiber der Wissens-Suchmaschine Wolfram Alpha hat seinem System die Erkennung von Bildern beigebracht. Mit der Funktion ImageIdentify bekommt man in Wolfram Language jetzt zu einem Bild eine symbolische Beschreibung des Inhalts, die sich in der Sprache danach sogar weiterverarbeiten lässt. Als Demo dieser Funktion dient die Website The Wolfram Language Image Identification Project: Dort kann man ein beliebiges Bild hochladen und sich das Ergebnis anschauen. Die Website speichert einen Thumbnail des hochgeladenen Bildes, sodass man einen Link zu der Ergebnisseite weitergeben kann. Wie so oft bei künstlicher Intelligenz sind die Ergebnisse manchmal lustig daneben, oft aber auch überraschend gut. Die Funktion arbeitet mit einem neuronalen Netz, das mit einigen -zig Millionen Bildern trainiert wurde und etwa 10.000 Objekte identifizieren kann.
    Content
    Vgl.: http://www.imageidentify.com. Eine ausführlichere Erklärung der Funktionsweise und Hintergründe findet sich in Stephen Wolframs Blog-Eintrag: "Wolfram Language Artificial Intelligence: The Image Identification Project" unter: http://blog.stephenwolfram.com/2015/05/wolfram-language-artificial-intelligence-the-image-identification-project/. Vgl. auch: https://news.ycombinator.com/item?id=8621658.
    Object
    Wolfram Language
    Source
    http://www.heise.de/newsticker/meldung/Wolfram-Language-erkennt-Bilder-2650207.html?wt_mc=rss.ho.beitrag.rdf
  6. Jones, K.P.: Natural-language processing and automatic indexing : a reply (1990) 0.04 
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  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) 0.03 
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    Abstract
    This paper presents a research on syntactic structures known as noun phrases (NP) being applied to increase the effectiveness and efficiency of the mechanisms for the document's classification. Our hypothesis is the fact that the NP can be used instead of single words as a semantic aggregator to reduce the number of words that will be used for the classification system without losing its semantic coverage, increasing its efficiency. The experiment divided the documents classification process in three phases: a) NP preprocessing b) system training; and c) classification experiments. In the first step, a corpus of digitalized texts was submitted to a natural language processing platform1 in which the part-of-speech tagging was done, and them PERL scripts pertaining to the PALAVRAS package were used to extract the Noun Phrases. The preprocessing also involved the tasks of a) removing NP low meaning pre-modifiers, as quantifiers; b) identification of synonyms and corresponding substitution for common hyperonyms; and c) stemming of the relevant words contained in the NP, for similitude checking with other NPs. The first tests with the resulting documents have demonstrated its effectiveness. We have compared the structural similarity of the documents before and after the whole pre-processing steps of phase one. The texts maintained the consistency with the original and have kept the readability. The second phase involves submitting the modified documents to a SVM algorithm to identify clusters and classify the documents. The classification rules are to be established using a machine learning approach. Finally, tests will be conducted to check the effectiveness of the whole process.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  8. Moens, M.F.: Automatic indexing and abstracting of document texts (2000) 0.03 
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    Content
    Need for indexing and abstracting texts; attributes of texts; text representations and their use; selection of natural language index terms; assignment of controlled language index texts; automatic abstracting; applications
  9. Kim, P.K.: ¬An automatic indexing of compound words based on mutual information for Korean text retrieval (1995) 0.03 
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    Abstract
    Presents an automatic indexing technique for compound words suitable for an agglutinative language, specifically Korean. Discusses some construction conditions for compound words and the rules for decomposing compound words to enhance the exhaustivity of indexing, demonstrating that this system, mutual information, enhances both the exhaustivity of indexing and the specifity of terms. Suggests that the construction conditions and rules for decomposition presented may be used in multilingual information retrieval systems to translate the indexing terms of the specific language into those of the language required
  10. Vledutz-Stokolov, N.: Concept recognition in an automatic text-processing system for the life sciences (1987) 0.03 
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    Abstract
    This article describes a natural-language text-processing system designed as an automatic aid to subject indexing at BIOSIS. The intellectual procedure the system should model is a deep indexing with a controlled vocabulary of biological concepts - Concept Headings (CHs). On the average, ten CHs are assigned to each article by BIOSIS indexers. The automatic procedure consists of two stages: (1) translation of natural-language biological titles into title-semantic representations which are in the constructed formalized language of Concept Primitives, and (2) translation of the latter representations into the language of CHs. The first stage is performed by matching the titles agianst the system's Semantic Vocabulary (SV). The SV currently contains approximately 15.000 biological natural-language terms and their translations in the language of Concept Primitives. Tor the ambiguous terms, the SV contains the algorithmical rules of term disambiguation, ruels based on semantic analysis of the contexts. The second stage of the automatic procedure is performed by matching the title representations against the CH definitions, formulated as Boolean search strategies in the language of Concept Primitives. Three experiments performed with the system and their results are decribed. The most typical problems the system encounters, the problems of lexical and situational ambiguities, are discussed. The disambiguation techniques employed are described and demonstrated in many examples
  11. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  12. Cohen, J.D.: Highlights: language- and domain-independent automatic indexing terms for abstracting (1995) 0.03 
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    Abstract
    Presents a model of drawing index terms from text. The approach uses no stop list, stemmer, or other language and domain specific component, allowing operation in any language or domain with only trivial modification. The method uses n-grams counts, achieving a function similar to, but more general than, a stemmer. The generated index terms, called 'highlights', are suitable for identifying the topic for perusal and selection. An extension is also described and demonstrated which selects index terms to represent a subset of documents, distinguishing them from the corpus. Presents some experimental results, showing operation in English, Spanish, German, Georgian, Russian and Japanese
  13. Taylor, S.L.: Integrating natural language understanding with document structure analysis (1994) 0.03 
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    Abstract
    Document understanding, the interpretation of a document from its image form, is a technology area which benefits greatly from the integration of natural language processing with image processing. Develops a prototype of an Intelligent Document Understanding System (IDUS) which employs several technologies: image processing, optical character recognition, document structure analysis and text understanding in a cooperative fashion. Discusses those areas of research during development of IDUS where it is found that the most benefit from the integration of natural language processing and image processing occured: document structure analysis, OCR correction, and text analysis. Discusses 2 applications which are supported by IDUS: text retrieval and automatic generation of hypertext links
  14. Silvester, J.P.; Genuardi, M.T.: Machine-aided indexing from the analysis of natural language text (1994) 0.03 
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  15. Kiros, R.; Salakhutdinov, R.; Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models (2014) 0.03 
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    Abstract
    Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.
  16. Koryconski, C.; Newell, A.F.: Natural-language processing and automatic indexing (1990) 0.02 
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    Abstract
    The task of producing satisfactory indexes by automatic means has been tackled on two fronts: by statistical analysis of text and by attempting content analysis of the text in much the same way as a human indexer does. Though statistical techniques have a lot to offer for free-text database systems, neither method has had much success with back-of-the-book indexing. This review examines some problems associated with the application of natural-language processing techniques to book texts. - Vgl. auch die Erwiderung von K.P. Jones
  17. Smart, G.: Using language analysis to manage information (1993) 0.02 
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    Abstract
    The ESPRIT project SIMPR developed software to analyse documents and generate indexes for them. Of immediate application as a document indexing and classification system, this also offers a technology for information modelling that has broader implications, supporting many new uses for information management softeware. The project was based on the assumption that information can only be managed successfully by computer systems that can view the information contained in a document through the language in which the document is written, and that systems need to be sufficiently flexible to respond to the changing requirements of document use
  18. Samstag-Schnock, U.; Meadow, C.T.: PBS: an ecomical natural language query interpreter (1993) 0.02 
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    Abstract
    Reports on the design and implementation of the information searching and retrieval software, PBS (Parsing, Boolean recognition, Stemming) for the front end OAK 2, a new version of OAK developed at Toronto Univ. OAK 2 is a research tool for user behaviour studies. PBS receives natural language search statements from an end user and identifies search facets and implied Boolean logic operators
  19. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.02 
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    Date
    20.10.2000 12:22:23
  20. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.02 
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    Source
    Information outlook. 9(2005) no.8, S.22-23

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