Search (68 results, page 1 of 4)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.25 
    0.2544547 = product of:
  0.4452957 = sum of:
    0.061370037 = product of:
      0.1841101 = sum of:
        0.1841101 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
          0.1841101 = score(doc=562,freq=2.0), product of:
            0.32758754 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03863967 = queryNorm
            0.56201804 = fieldWeight in 562, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=562)
      0.33333334 = coord(1/3)
    0.1841101 = weight(_text_:2f in 562) [ClassicSimilarity], result of:
      0.1841101 = score(doc=562,freq=2.0), product of:
        0.32758754 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03863967 = queryNorm
        0.56201804 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
    0.1841101 = weight(_text_:2f in 562) [ClassicSimilarity], result of:
      0.1841101 = score(doc=562,freq=2.0), product of:
        0.32758754 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03863967 = queryNorm
        0.56201804 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
    0.015705423 = product of:
      0.031410847 = sum of:
        0.031410847 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
          0.031410847 = score(doc=562,freq=2.0), product of:
            0.13530953 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03863967 = queryNorm
            0.23214069 = fieldWeight in 562, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=562)
      0.5 = coord(1/2)
  0.5714286 = coord(4/7)
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.18 
    0.1841101 = product of:
  0.42959026 = sum of:
    0.061370037 = product of:
      0.1841101 = sum of:
        0.1841101 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.1841101 = score(doc=862,freq=2.0), product of:
            0.32758754 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03863967 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
      0.33333334 = coord(1/3)
    0.1841101 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
      0.1841101 = score(doc=862,freq=2.0), product of:
        0.32758754 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03863967 = queryNorm
        0.56201804 = fieldWeight in 862, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=862)
    0.1841101 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
      0.1841101 = score(doc=862,freq=2.0), product of:
        0.32758754 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03863967 = queryNorm
        0.56201804 = fieldWeight in 862, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=862)
  0.42857143 = coord(3/7)
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.16 
    0.16453956 = product of:
  0.38392565 = sum of:
    0.1841101 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.1841101 = score(doc=563,freq=2.0), product of:
        0.32758754 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03863967 = queryNorm
        0.56201804 = fieldWeight in 563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=563)
    0.1841101 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.1841101 = score(doc=563,freq=2.0), product of:
        0.32758754 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03863967 = queryNorm
        0.56201804 = fieldWeight in 563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=563)
    0.015705423 = product of:
      0.031410847 = sum of:
        0.031410847 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
          0.031410847 = score(doc=563,freq=2.0), product of:
            0.13530953 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03863967 = queryNorm
            0.23214069 = fieldWeight in 563, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=563)
      0.5 = coord(1/2)
  0.42857143 = coord(3/7)
    Content
    A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
    Date
    10. 1.2013 19:22:47
  4. Schwarz, C.: THESYS: Thesaurus Syntax System : a fully automatic thesaurus building aid (1988) 0.04 
    0.04419798 = product of:
  0.15469292 = sum of:
    0.13636993 = weight(_text_:assisted in 1361) [ClassicSimilarity], result of:
      0.13636993 = score(doc=1361,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.52244925 = fieldWeight in 1361, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1361)
    0.018322995 = product of:
      0.03664599 = sum of:
        0.03664599 = weight(_text_:22 in 1361) [ClassicSimilarity], result of:
          0.03664599 = score(doc=1361,freq=2.0), product of:
            0.13530953 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03863967 = queryNorm
            0.2708308 = fieldWeight in 1361, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1361)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)
    Abstract
    THESYS is based on the natural language processing of free-text databases. It yields statistically evaluated correlations between words of the database. These correlations correspond to traditional thesaurus relations. The person who has to build a thesaurus is thus assisted by the proposals made by THESYS. THESYS is being tested on commercial databases under real world conditions. It is part of a text processing project at Siemens, called TINA (Text-Inhalts-Analyse). Software from TINA is actually being applied and evaluated by the US Department of Commerce for patent search and indexing (REALIST: REtrieval Aids by Linguistics and STatistics)
    Date
    6. 1.1999 10:22:07
  5. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.03 
    0.027550887 = product of:
  0.19285621 = sum of:
    0.19285621 = weight(_text_:assisted in 1139) [ClassicSimilarity], result of:
      0.19285621 = score(doc=1139,freq=4.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.7388549 = fieldWeight in 1139, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1139)
  0.14285715 = coord(1/7)
    Abstract
    In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
  6. Oard, D.W.; He, D.; Wang, J.: User-assisted query translation for interactive cross-language information retrieval (2008) 0.02 
    0.02361505 = product of:
  0.16530533 = sum of:
    0.16530533 = weight(_text_:assisted in 2030) [ClassicSimilarity], result of:
      0.16530533 = score(doc=2030,freq=4.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.6333042 = fieldWeight in 2030, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.046875 = fieldNorm(doc=2030)
  0.14285715 = coord(1/7)
    Abstract
    Interactive Cross-Language Information Retrieval (CLIR), a process in which searcher and system collaborate to find documents that satisfy an information need regardless of the language in which those documents are written, calls for designs in which synergies between searcher and system can be leveraged so that the strengths of one can cover weaknesses of the other. This paper describes an approach that employs user-assisted query translation to help searchers better understand the system's operation. Supporting interaction and interface designs are introduced, and results from three user studies are presented. The results indicate that experienced searchers presented with this new system evolve new search strategies that make effective use of the new capabilities, that they achieve retrieval effectiveness comparable to results obtained using fully automatic techniques, and that reported satisfaction with support for cross-language searching increased. The paper concludes with a description of a freely available interactive CLIR system that incorporates lessons learned from this research.
  7. Polity, Y.: Vers une ergonomie linguistique (1994) 0.02 
    0.022264479 = product of:
  0.15585135 = sum of:
    0.15585135 = weight(_text_:assisted in 36) [ClassicSimilarity], result of:
      0.15585135 = score(doc=36,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.5970849 = fieldWeight in 36, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.0625 = fieldNorm(doc=36)
  0.14285715 = coord(1/7)
    Abstract
    Analyzed a special type of man-mchine interaction, that of searching an information system with natural language. A model for full text processing for information retrieval was proposed that considered the system's users and how they employ information. Describes how INIST (the National Institute for Scientific and Technical Information) is developing computer assisted indexing as an aid to improving relevance when retrieving information from bibliographic data banks
  8. Gillaspie, L.: ¬The role of linguistic phenomena in retrieval performance (1995) 0.02 
    0.022264479 = product of:
  0.15585135 = sum of:
    0.15585135 = weight(_text_:assisted in 3861) [ClassicSimilarity], result of:
      0.15585135 = score(doc=3861,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.5970849 = fieldWeight in 3861, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.0625 = fieldNorm(doc=3861)
  0.14285715 = coord(1/7)
    Abstract
    This progress report presents findings from a failure analysis of 2 commercial full text computer assisted legal research (CALR) systems. Linguistic analyzes of unretrieved documents als false drops reveal a number of potential causes for performance problems in these databases, ranging from synonymy and homography to discourse level cohesive relations. Ecxamines and discusses examples of natural language phenomena that affects Boolean retrieval system performance
  9. Armstrong, G.: Computer-assisted literary analysis using the TACT a text-retrieval program (1996) 0.02 
    0.022264479 = product of:
  0.15585135 = sum of:
    0.15585135 = weight(_text_:assisted in 5690) [ClassicSimilarity], result of:
      0.15585135 = score(doc=5690,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.5970849 = fieldWeight in 5690, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.0625 = fieldNorm(doc=5690)
  0.14285715 = coord(1/7)
  10. ChatGPT : Optimizing language models for dalogue (2022) 0.02 
    0.017184932 = product of:
  0.12029452 = sum of:
    0.12029452 = weight(_text_:instruction in 836) [ClassicSimilarity], result of:
      0.12029452 = score(doc=836,freq=2.0), product of:
        0.2293201 = queryWeight, product of:
          5.934836 = idf(docFreq=317, maxDocs=44218)
          0.03863967 = queryNorm
        0.52457035 = fieldWeight in 836, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.934836 = idf(docFreq=317, maxDocs=44218)
          0.0625 = fieldNorm(doc=836)
  0.14285715 = coord(1/7)
    Abstract
    We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.
  11. Jaaranen, K.; Lehtola, A.; Tenni, J.; Bounsaythip, C.: Webtran tools for in-company language support (2000) 0.02 
    0.016698359 = product of:
  0.11688851 = sum of:
    0.11688851 = weight(_text_:assisted in 5553) [ClassicSimilarity], result of:
      0.11688851 = score(doc=5553,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.44781366 = fieldWeight in 5553, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.046875 = fieldNorm(doc=5553)
  0.14285715 = coord(1/7)
    Abstract
    Webtran tools for authoring and translating domain specific texts can make the multilingual text production in a company more efficient and less expensive. Tile tools have been in production use since spring 2000 for checking and translating product article texts of a specific domain, namely an in-company language in sales catalogues of a mail-order company. Webtran tools have been developed by VTT Information Technology. Use experiences have shown that an automatic translation process is faster than phrase-lexicon assisted manual translation, if an in-company language model is created to control and support the language used within the company
  12. Anguiano Peña, G.; Naumis Peña, C.: Method for selecting specialized terms from a general language corpus (2015) 0.02 
    0.016698359 = product of:
  0.11688851 = sum of:
    0.11688851 = weight(_text_:assisted in 2196) [ClassicSimilarity], result of:
      0.11688851 = score(doc=2196,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.44781366 = fieldWeight in 2196, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.046875 = fieldNorm(doc=2196)
  0.14285715 = coord(1/7)
    Abstract
    Among the many aspects studied by library and information science are linguistic phenomena associated with document content analysis, for purposes of both information organization and retrieval. To this end, terms used in scientific and technical language must be recovered and their area of domain and behavior studied. Through language, society controls the knowledge available to people. Document content analysis, in this case of scientific texts, facilitates gathering knowledge of lexical units and their major applications and separating such specialized terms from the general language, to create indexing languages. The model presented here or other lexicographic resources with similar characteristics may be useful in the near future, in computer-assisted indexing or as corpora monitors, with respect to new text analyses or specialized corpora. Thus, using techniques for document content analysis of a lexicographically labeled general language corpus proposed herein, components which enable the extraction of lexical units from specialized language may be obtained and characterized.
  13. From information to knowledge : conceptual and content analysis by computer (1995) 0.01 
    0.0139153 = product of:
  0.097407095 = sum of:
    0.097407095 = weight(_text_:assisted in 5392) [ClassicSimilarity], result of:
      0.097407095 = score(doc=5392,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.37317806 = fieldWeight in 5392, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5392)
  0.14285715 = coord(1/7)
    Content
    SCHMIDT, K.M.: Concepts - content - meaning: an introduction; DUCHASTEL, J. et al.: The SACAO project: using computation toward textual data analysis; PAQUIN, L.-C. u. L. DUPUY: An approach to expertise transfer: computer-assisted text analysis; HOGENRAAD, R., Y. BESTGEN u. J.-L. NYSTEN: Terrorist rhetoric: texture and architecture; MOHLER, P.P.: On the interaction between reading and computing: an interpretative approach to content analysis; LANCASHIRE, I.: Computer tools for cognitive stylistics; MERGENTHALER, E.: An outline of knowledge based text analysis; NAMENWIRTH, J.Z.: Ideography in computer-aided content analysis; WEBER, R.P. u. J.Z. Namenwirth: Content-analytic indicators: a self-critique; McKINNON, A.: Optimizing the aberrant frequency word technique; ROSATI, R.: Factor analysis in classical archaeology: export patterns of Attic pottery trade; PETRILLO, P.S.: Old and new worlds: ancient coinage and modern technology; DARANYI, S., S. MARJAI u.a.: Caryatids and the measurement of semiosis in architecture; ZARRI, G.P.: Intelligent information retrieval: an application in the field of historical biographical data; BOUCHARD, G., R. ROY u.a.: Computers and genealogy: from family reconstitution to population reconstruction; DEMÉLAS-BOHY, M.-D. u. M. RENAUD: Instability, networks and political parties: a political history expert system prototype; DARANYI, S., A. ABRANYI u. G. KOVACS: Knowledge extraction from ethnopoetic texts by multivariate statistical methods; FRAUTSCHI, R.L.: Measures of narrative voice in French prose fiction applied to textual samples from the enlightenment to the twentieth century; DANNENBERG, R. u.a.: A project in computer music: the musician's workbench
  14. Melucci, M.; Orio, N.: Design, implementation, and evaluation of a methodology for automatic stemmer generation (2007) 0.01 
    0.013535535 = product of:
  0.09474874 = sum of:
    0.09474874 = weight(_text_:great in 268) [ClassicSimilarity], result of:
      0.09474874 = score(doc=268,freq=2.0), product of:
        0.21757144 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.03863967 = queryNorm
        0.43548337 = fieldWeight in 268, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.0546875 = fieldNorm(doc=268)
  0.14285715 = coord(1/7)
    Abstract
    The authors describe a statistical approach based on hidden Markov models (HMMs), for generating stemmers automatically. The proposed approach requires little effort to insert new languages in the system even if minimal linguistic knowledge is available. This is a key advantage especially for digital libraries, which are often developed for a specific institution or government because the program can manage a great amount of documents written in local languages. The evaluation described in the article shows that the stemmers implemented by means of HMMs are as effective as those based on linguistic rules.
  15. Kreymer, O.: ¬An evaluation of help mechanisms in natural language information retrieval systems (2002) 0.01 
    0.012888699 = product of:
  0.09022089 = sum of:
    0.09022089 = weight(_text_:instruction in 2557) [ClassicSimilarity], result of:
      0.09022089 = score(doc=2557,freq=2.0), product of:
        0.2293201 = queryWeight, product of:
          5.934836 = idf(docFreq=317, maxDocs=44218)
          0.03863967 = queryNorm
        0.39342776 = fieldWeight in 2557, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.934836 = idf(docFreq=317, maxDocs=44218)
          0.046875 = fieldNorm(doc=2557)
  0.14285715 = coord(1/7)
    Abstract
    The field of natural language processing (NLP) demonstrates rapid changes in the design of information retrieval systems and human-computer interaction. While natural language is being looked on as the most effective tool for information retrieval in a contemporary information environment, the systems using it are only beginning to emerge. This study attempts to evaluate the current state of NLP information retrieval systems from the user's point of view: what techniques are used by these systems to guide their users through the search process? The analysis focused on the structure and components of the systems' help mechanisms. Results of the study demonstrated that systems which claimed to be using natural language searching in fact used a wide range of information retrieval techniques from real natural language processing to Boolean searching. As a result, the user assistance mechanisms of these systems also varied. While pseudo-NLP systems would suit a more traditional method of instruction, real NLP systems primarily utilised the methods of explanation and user-system dialogue.
  16. Peis, E.; Herrera-Viedma, E.; Herrera, J.C.: On the evaluation of XML documents using Fuzzy linguistic techniques (2003) 0.01 
    0.011601887 = product of:
  0.081213206 = sum of:
    0.081213206 = weight(_text_:great in 2778) [ClassicSimilarity], result of:
      0.081213206 = score(doc=2778,freq=2.0), product of:
        0.21757144 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.03863967 = queryNorm
        0.37327147 = fieldWeight in 2778, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.046875 = fieldNorm(doc=2778)
  0.14285715 = coord(1/7)
    Abstract
    Recommender systems evaluate and filter the great amount of information available an the Web to assist people in their search processes. A fuzzy evaluation method of XML documents based an computing with words is presented. Given an XML document type (e.g. scientific article), we consider that its elements are not equally informative. This is indicated by the use of a DTD and defining linguistic importance attributes to the more meaningful elements of the DTD designed. Then, the evaluation method generates linguistic recommendations from linguistic evaluation judgements provided by different recommenders an meaningful elements of DTD.
  17. Ramisch, C.: Multiword expressions acquisition : a generic and open framework (2015) 0.01 
    0.011132239 = product of:
  0.077925675 = sum of:
    0.077925675 = weight(_text_:assisted in 1649) [ClassicSimilarity], result of:
      0.077925675 = score(doc=1649,freq=2.0), product of:
        0.26102042 = queryWeight, product of:
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03863967 = queryNorm
        0.29854244 = fieldWeight in 1649, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.7552447 = idf(docFreq=139, maxDocs=44218)
          0.03125 = fieldNorm(doc=1649)
  0.14285715 = coord(1/7)
    Abstract
    This book is an excellent introduction to multiword expressions. It provides a unique, comprehensive and up-to-date overview of this exciting topic in computational linguistics. The first part describes the diversity and richness of multiword expressions, including many examples in several languages. These constructions are not only complex and arbitrary, but also much more frequent than one would guess, making them a real nightmare for natural language processing applications. The second part introduces a new generic framework for automatic acquisition of multiword expressions from texts. Furthermore, it describes the accompanying free software tool, the mwetoolkit, which comes in handy when looking for expressions in texts (regardless of the language). Evaluation is greatly emphasized, underlining the fact that results depend on parameters like corpus size, language, MWE type, etc. The last part contains solid experimental results and evaluates the mwetoolkit, demonstrating its usefulness for computer-assisted lexicography and machine translation. This is the first book to cover the whole pipeline of multiword expression acquisition in a single volume. It is addresses the needs of students and researchers in computational and theoretical linguistics, cognitive sciences, artificial intelligence and computer science. Its good balance between computational and linguistic views make it the perfect starting point for anyone interested in multiword expressions, language and text processing in general.
  18. Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012) 0.01 
    0.009668238 = product of:
  0.06767767 = sum of:
    0.06767767 = weight(_text_:great in 2861) [ClassicSimilarity], result of:
      0.06767767 = score(doc=2861,freq=2.0), product of:
        0.21757144 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.03863967 = queryNorm
        0.31105953 = fieldWeight in 2861, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2861)
  0.14285715 = coord(1/7)
    Abstract
    Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.
  19. Rozinajová, V.; Macko, P.: Using natural language to search linked data (2017) 0.01 
    0.009668238 = product of:
  0.06767767 = sum of:
    0.06767767 = weight(_text_:great in 3488) [ClassicSimilarity], result of:
      0.06767767 = score(doc=3488,freq=2.0), product of:
        0.21757144 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.03863967 = queryNorm
        0.31105953 = fieldWeight in 3488, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3488)
  0.14285715 = coord(1/7)
    Abstract
    There are many endeavors aiming to offer users more effective ways of getting relevant information from web. One of them is represented by a concept of Linked Data, which provides interconnected data sources. But querying these types of data is difficult not only for the conventional web users but also for ex-perts in this field. Therefore, a more comfortable way of user query would be of great value. One direction could be to allow the user to use a natural language. To make this task easier we have proposed a method for translating natural language query to SPARQL query. It is based on a sentence structure - utilizing dependen-cies between the words in user queries. Dependencies are used to map the query to the semantic web structure, which is in the next step translated to SPARQL query. According to our first experiments we are able to answer a significant group of user queries.
  20. Ali, C.B.; Haddad, H.; Slimani, Y.: Multi-word terms selection for information retrieval (2022) 0.01 
    0.009668238 = product of:
  0.06767767 = sum of:
    0.06767767 = weight(_text_:great in 900) [ClassicSimilarity], result of:
      0.06767767 = score(doc=900,freq=2.0), product of:
        0.21757144 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.03863967 = queryNorm
        0.31105953 = fieldWeight in 900, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=900)
  0.14285715 = coord(1/7)
    Abstract
    Purpose A number of approaches and algorithms have been proposed over the years as a basis for automatic indexing. Many of these approaches suffer from precision inefficiency at low recall. The choice of indexing units has a great impact on search system effectiveness. The authors dive beyond simple terms indexing to propose a framework for multi-word terms (MWT) filtering and indexing. Design/methodology/approach In this paper, the authors rely on ranking MWT to filter them, keeping the most effective ones for the indexing process. The proposed model is based on filtering MWT according to their ability to capture the document topic and distinguish between different documents from the same collection. The authors rely on the hypothesis that the best MWT are those that achieve the greatest association degree. The experiments are carried out with English and French languages data sets. Findings The results indicate that this approach achieved precision enhancements at low recall, and it performed better than more advanced models based on terms dependencies. Originality/value Using and testing different association measures to select MWT that best describe the documents to enhance the precision in the first retrieved documents.

Authors

Years

Languages

  • e 51
  • d 16
  • f 1
  • More… Less…

Types

  • a 53
  • el 7
  • m 6
  • s 4
  • p 2
  • x 2
  • d 1
  • More… Less…

Themes

Subjects

Classifications