Search (572 results, page 1 of 29)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.26
    0.25871408 = product of:
      0.46568534 = sum of:
        0.062223002 = product of:
          0.186669 = sum of:
            0.186669 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
              0.186669 = score(doc=562,freq=2.0), product of:
                0.3321406 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.03917671 = 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)
        0.186669 = weight(_text_:2f in 562) [ClassicSimilarity], result of:
          0.186669 = score(doc=562,freq=2.0), product of:
            0.3321406 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03917671 = 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)
        0.014200641 = weight(_text_:of in 562) [ClassicSimilarity], result of:
          0.014200641 = score(doc=562,freq=10.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.23179851 = fieldWeight in 562, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=562)
        0.186669 = weight(_text_:2f in 562) [ClassicSimilarity], result of:
          0.186669 = score(doc=562,freq=2.0), product of:
            0.3321406 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03917671 = 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)
        0.015923709 = product of:
          0.031847417 = sum of:
            0.031847417 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
              0.031847417 = score(doc=562,freq=2.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = 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)
      0.5555556 = coord(5/9)
    
    Abstract
    Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
    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
    Source
    Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.20
    0.19989406 = product of:
      0.44976163 = sum of:
        0.062223002 = product of:
          0.186669 = sum of:
            0.186669 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
              0.186669 = score(doc=862,freq=2.0), product of:
                0.3321406 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.03917671 = 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)
        0.186669 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
          0.186669 = score(doc=862,freq=2.0), product of:
            0.3321406 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03917671 = 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)
        0.014200641 = weight(_text_:of in 862) [ClassicSimilarity], result of:
          0.014200641 = score(doc=862,freq=10.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.23179851 = fieldWeight in 862, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
        0.186669 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
          0.186669 = score(doc=862,freq=2.0), product of:
            0.3321406 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03917671 = 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)
      0.44444445 = coord(4/9)
    
    Abstract
    This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
    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) 0.18
    0.18098857 = product of:
      0.40722427 = sum of:
        0.186669 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
          0.186669 = score(doc=563,freq=2.0), product of:
            0.3321406 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03917671 = 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)
        0.017962547 = weight(_text_:of in 563) [ClassicSimilarity], result of:
          0.017962547 = score(doc=563,freq=16.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.2932045 = fieldWeight in 563, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=563)
        0.186669 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
          0.186669 = score(doc=563,freq=2.0), product of:
            0.3321406 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03917671 = 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)
        0.015923709 = product of:
          0.031847417 = sum of:
            0.031847417 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
              0.031847417 = score(doc=563,freq=2.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = 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)
      0.44444445 = coord(4/9)
    
    Abstract
    In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.
    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
    Imprint
    Guelph, Ontario : University of Guelph
  4. Haas, S.W.: Natural language processing : toward large-scale, robust systems (1996) 0.08
    0.080591865 = product of:
      0.1813317 = sum of:
        0.09491582 = weight(_text_:applications in 7415) [ClassicSimilarity], result of:
          0.09491582 = score(doc=7415,freq=4.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.5503137 = fieldWeight in 7415, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0625 = fieldNorm(doc=7415)
        0.018934188 = weight(_text_:of in 7415) [ClassicSimilarity], result of:
          0.018934188 = score(doc=7415,freq=10.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.3090647 = fieldWeight in 7415, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=7415)
        0.046250064 = weight(_text_:systems in 7415) [ClassicSimilarity], result of:
          0.046250064 = score(doc=7415,freq=4.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.38414678 = fieldWeight in 7415, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0625 = fieldNorm(doc=7415)
        0.021231614 = product of:
          0.042463228 = sum of:
            0.042463228 = weight(_text_:22 in 7415) [ClassicSimilarity], result of:
              0.042463228 = score(doc=7415,freq=2.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = queryNorm
                0.30952093 = fieldWeight in 7415, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=7415)
          0.5 = coord(1/2)
      0.44444445 = coord(4/9)
    
    Abstract
    State of the art review of natural language processing updating an earlier review published in ARIST 22(1987). Discusses important developments that have allowed for significant advances in the field of natural language processing: materials and resources; knowledge based systems and statistical approaches; and a strong emphasis on evaluation. Reviews some natural language processing applications and common problems still awaiting solution. Considers closely related applications such as language generation and th egeneration phase of machine translation which face the same problems as natural language processing. Covers natural language methodologies for information retrieval only briefly
    Source
    Annual review of information science and technology. 31(1996), S.83-119
  5. Lonsdale, D.; Mitamura, T.; Nyberg, E.: Acquisition of large lexicons for practical knowledge-based MT (1994/95) 0.08
    0.07804743 = product of:
      0.17560673 = sum of:
        0.07118686 = weight(_text_:applications in 7409) [ClassicSimilarity], result of:
          0.07118686 = score(doc=7409,freq=4.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.41273528 = fieldWeight in 7409, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.046875 = fieldNorm(doc=7409)
        0.021062955 = weight(_text_:of in 7409) [ClassicSimilarity], result of:
          0.021062955 = score(doc=7409,freq=22.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.34381276 = fieldWeight in 7409, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=7409)
        0.042483397 = weight(_text_:systems in 7409) [ClassicSimilarity], result of:
          0.042483397 = score(doc=7409,freq=6.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.35286134 = fieldWeight in 7409, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=7409)
        0.040873505 = weight(_text_:software in 7409) [ClassicSimilarity], result of:
          0.040873505 = score(doc=7409,freq=2.0), product of:
            0.15541996 = queryWeight, product of:
              3.9671519 = idf(docFreq=2274, maxDocs=44218)
              0.03917671 = queryNorm
            0.2629875 = fieldWeight in 7409, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.9671519 = idf(docFreq=2274, maxDocs=44218)
              0.046875 = fieldNorm(doc=7409)
      0.44444445 = coord(4/9)
    
    Abstract
    Although knowledge based MT systems have the potential to achieve high translation accuracy, each successful application system requires a large amount of hand coded lexical knowledge. Systems like KBMT-89 and its descendants have demonstarted how knowledge based translation can produce good results in technical domains with tractable domain semantics. Nevertheless, the magnitude of the development task for large scale applications with 10s of 1000s of of domain concepts precludes a purely hand crafted approach. The current challenge for the next generation of knowledge based MT systems is to utilize online textual resources and corpus analysis software in order to automate the most laborious aspects of the knowledge acquisition process. This partial automation can in turn maximize the productivity of human knowledge engineers and help to make large scale applications of knowledge based MT an viable approach. Discusses the corpus based knowledge acquisition methodology used in KANT, a knowledge based translation system for multilingual document production. This methodology can be generalized beyond the KANT interlinhua approach for use with any system that requires similar kinds of knowledge
  6. Goshawke, W.; Kelly, D.K.; Wigg, J.D.: Computer translation of natural language (1987) 0.08
    0.077916116 = product of:
      0.23374835 = sum of:
        0.14237373 = weight(_text_:applications in 4819) [ClassicSimilarity], result of:
          0.14237373 = score(doc=4819,freq=4.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.82547057 = fieldWeight in 4819, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.09375 = fieldNorm(doc=4819)
        0.021999538 = weight(_text_:of in 4819) [ClassicSimilarity], result of:
          0.021999538 = score(doc=4819,freq=6.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.3591007 = fieldWeight in 4819, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.09375 = fieldNorm(doc=4819)
        0.06937509 = weight(_text_:systems in 4819) [ClassicSimilarity], result of:
          0.06937509 = score(doc=4819,freq=4.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.57622015 = fieldWeight in 4819, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.09375 = fieldNorm(doc=4819)
      0.33333334 = coord(3/9)
    
    PRECIS
    Languages / Translation / Applications of computer systems
    Subject
    Languages / Translation / Applications of computer systems
  7. Czejdo. B.D.; Tucci, R.P.: ¬A dataflow graphical language for database applications (1994) 0.07
    0.06587547 = product of:
      0.19762641 = sum of:
        0.118644774 = weight(_text_:applications in 559) [ClassicSimilarity], result of:
          0.118644774 = score(doc=559,freq=4.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.68789214 = fieldWeight in 559, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.078125 = fieldNorm(doc=559)
        0.021169065 = weight(_text_:of in 559) [ClassicSimilarity], result of:
          0.021169065 = score(doc=559,freq=8.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.34554482 = fieldWeight in 559, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.078125 = fieldNorm(doc=559)
        0.05781258 = weight(_text_:systems in 559) [ClassicSimilarity], result of:
          0.05781258 = score(doc=559,freq=4.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.48018348 = fieldWeight in 559, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.078125 = fieldNorm(doc=559)
      0.33333334 = coord(3/9)
    
    Abstract
    Discusses a graphical language for information retrieval and processing. A lot of recent activity has occured in the area of improving access to database systems. However, current results are restricted to simple interfacing of database systems. Proposes a graphical language for specifying complex applications
    Source
    CIT - Journal of computing and information technology. 2(1994) no.1, S.39-50
  8. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.06
    0.06319208 = product of:
      0.14218217 = sum of:
        0.08389453 = weight(_text_:applications in 2541) [ClassicSimilarity], result of:
          0.08389453 = score(doc=2541,freq=8.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.4864132 = fieldWeight in 2541, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2541)
        0.019081537 = weight(_text_:of in 2541) [ClassicSimilarity], result of:
          0.019081537 = score(doc=2541,freq=26.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.31146988 = fieldWeight in 2541, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2541)
        0.020439833 = weight(_text_:systems in 2541) [ClassicSimilarity], result of:
          0.020439833 = score(doc=2541,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.1697705 = fieldWeight in 2541, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2541)
        0.018766273 = product of:
          0.037532546 = sum of:
            0.037532546 = weight(_text_:22 in 2541) [ClassicSimilarity], result of:
              0.037532546 = score(doc=2541,freq=4.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = queryNorm
                0.27358043 = fieldWeight in 2541, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2541)
          0.5 = coord(1/2)
      0.44444445 = coord(4/9)
    
    Abstract
    The Specialized Information Services Division (SIS) of the National Library of Medicine (NLM) provides Web access to more than a dozen scientific databases on toxicology and the environment on TOXNET . Search queries on TOXNET often include misspelled or variant English words, medical and scientific jargon and chemical names. Following the example of search engines like Google and ClinicalTrials.gov, we set out to develop a spelling "suggestion" system for increased recall and precision in TOXNET searching. This paper describes development of dictionary technology that can be used in a variety of applications such as orthographic verification, writing aid, natural language processing, and information storage and retrieval. The design of the technology allows building complex applications using the components developed in the earlier phases of the work in a modular fashion without extensive rewriting of computer code. Since many of the potential applications envisioned for this work have on-line or web-based interfaces, the dictionaries and other computer components must have fast response, and must be adaptable to open-ended database vocabularies, including chemical nomenclature. The dictionary vocabulary for this work was derived from SIS and other databases and specialized resources, such as NLM's Unified Medical Language Systems (UMLS) . The resulting technology, A-Z Dictionary (AZdict), has three major constituents: 1) the vocabulary list, 2) the word attributes that define part of speech and morphological relationships between words in the list, and 3) a set of programs that implements the retrieval of words and their attributes, and determines similarity between words (ChemSpell). These three components can be used in various applications such as spelling verification, spelling aid, part-of-speech tagging, paraphrasing, and many other natural language processing functions.
    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29
  9. Liddy, E.D.: Natural language processing for information retrieval and knowledge discovery (1998) 0.06
    0.05638929 = product of:
      0.1268759 = sum of:
        0.05872617 = weight(_text_:applications in 2345) [ClassicSimilarity], result of:
          0.05872617 = score(doc=2345,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.34048924 = fieldWeight in 2345, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2345)
        0.020956306 = weight(_text_:of in 2345) [ClassicSimilarity], result of:
          0.020956306 = score(doc=2345,freq=16.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.34207192 = fieldWeight in 2345, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2345)
        0.028615767 = weight(_text_:systems in 2345) [ClassicSimilarity], result of:
          0.028615767 = score(doc=2345,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.23767869 = fieldWeight in 2345, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2345)
        0.018577661 = product of:
          0.037155323 = sum of:
            0.037155323 = weight(_text_:22 in 2345) [ClassicSimilarity], result of:
              0.037155323 = score(doc=2345,freq=2.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = queryNorm
                0.2708308 = fieldWeight in 2345, 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=2345)
          0.5 = coord(1/2)
      0.44444445 = coord(4/9)
    
    Abstract
    Natural language processing (NLP) is a powerful technology for the vital tasks of information retrieval (IR) and knowledge discovery (KD) which, in turn, feed the visualization systems of the present and future and enable knowledge workers to focus more of their time on the vital tasks of analysis and prediction
    Date
    22. 9.1997 19:16:05
    Imprint
    Urbana-Champaign, IL : Illinois University at Urbana-Champaign, Graduate School of Library and Information Science
    Source
    Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al
  10. Rettinger, A.; Schumilin, A.; Thoma, S.; Ell, B.: Learning a cross-lingual semantic representation of relations expressed in text (2015) 0.05
    0.045119576 = product of:
      0.13535872 = sum of:
        0.08389453 = weight(_text_:applications in 2027) [ClassicSimilarity], result of:
          0.08389453 = score(doc=2027,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.4864132 = fieldWeight in 2027, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.078125 = fieldNorm(doc=2027)
        0.010584532 = weight(_text_:of in 2027) [ClassicSimilarity], result of:
          0.010584532 = score(doc=2027,freq=2.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.17277241 = fieldWeight in 2027, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.078125 = fieldNorm(doc=2027)
        0.040879667 = weight(_text_:systems in 2027) [ClassicSimilarity], result of:
          0.040879667 = score(doc=2027,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.339541 = fieldWeight in 2027, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.078125 = fieldNorm(doc=2027)
      0.33333334 = coord(3/9)
    
    Series
    Information Systems and Applications, incl. Internet/Web, and HCI; Bd. 9088
  11. Rahmstorf, G.: Compositional semantics and concept representation (1991) 0.04
    0.044702347 = product of:
      0.13410704 = sum of:
        0.06711562 = weight(_text_:applications in 6673) [ClassicSimilarity], result of:
          0.06711562 = score(doc=6673,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.38913056 = fieldWeight in 6673, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0625 = fieldNorm(doc=6673)
        0.020741362 = weight(_text_:of in 6673) [ClassicSimilarity], result of:
          0.020741362 = score(doc=6673,freq=12.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.33856338 = fieldWeight in 6673, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=6673)
        0.046250064 = weight(_text_:systems in 6673) [ClassicSimilarity], result of:
          0.046250064 = score(doc=6673,freq=4.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.38414678 = fieldWeight in 6673, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0625 = fieldNorm(doc=6673)
      0.33333334 = coord(3/9)
    
    Abstract
    Concept systems are not only used in the sciences, but also in secondary supporting fields, e.g. in libraries, in documentation, in terminology and increasingly also in knowledge representation. It is suggested that the development of concept systems be based on semantic analysis. Methodical steps are described. The principle of morpho-syntactic composition in semantics will serve as a theoretical basis for the suggested method. The implications and limitations of this principle will be demonstrated
    Source
    Classification, data analysis, and knowledge organization: models and methods with applications. Proc. of the 14th annual conf. of the Gesellschaft für Klassifikation, Univ. of Marburg, 12.-14.3.1990. Ed.: H.-H. Bock u. P. Ihm
  12. Croft, W.B.: Knowledge-based and statistical approaches to text retrieval (1993) 0.04
    0.044364154 = product of:
      0.1996387 = sum of:
        0.13423124 = weight(_text_:applications in 7863) [ClassicSimilarity], result of:
          0.13423124 = score(doc=7863,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.7782611 = fieldWeight in 7863, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.125 = fieldNorm(doc=7863)
        0.06540746 = weight(_text_:systems in 7863) [ClassicSimilarity], result of:
          0.06540746 = score(doc=7863,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.5432656 = fieldWeight in 7863, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.125 = fieldNorm(doc=7863)
      0.22222222 = coord(2/9)
    
    Source
    IEEE expert intelligent systems and their applications. 8(1993) no.2, S.8-12
  13. Computational linguistics for the new millennium : divergence or synergy? Proceedings of the International Symposium held at the Ruprecht-Karls Universität Heidelberg, 21-22 July 2000. Festschrift in honour of Peter Hellwig on the occasion of his 60th birthday (2002) 0.04
    0.04177325 = product of:
      0.093989804 = sum of:
        0.041947264 = weight(_text_:applications in 4900) [ClassicSimilarity], result of:
          0.041947264 = score(doc=4900,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.2432066 = fieldWeight in 4900, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4900)
        0.018332949 = weight(_text_:of in 4900) [ClassicSimilarity], result of:
          0.018332949 = score(doc=4900,freq=24.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.2992506 = fieldWeight in 4900, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4900)
        0.020439833 = weight(_text_:systems in 4900) [ClassicSimilarity], result of:
          0.020439833 = score(doc=4900,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.1697705 = fieldWeight in 4900, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4900)
        0.013269759 = product of:
          0.026539518 = sum of:
            0.026539518 = weight(_text_:22 in 4900) [ClassicSimilarity], result of:
              0.026539518 = score(doc=4900,freq=2.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = queryNorm
                0.19345059 = fieldWeight in 4900, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4900)
          0.5 = coord(1/2)
      0.44444445 = coord(4/9)
    
    Abstract
    The two seemingly conflicting tendencies, synergy and divergence, are both fundamental to the advancement of any science. Their interplay defines the demarcation line between application-oriented and theoretical research. The papers in this festschrift in honour of Peter Hellwig are geared to answer questions that arise from this insight: where does the discipline of Computational Linguistics currently stand, what has been achieved so far and what should be done next. Given the complexity of such questions, no simple answers can be expected. However, each of the practitioners and researchers are contributing from their very own perspective a piece of insight into the overall picture of today's and tomorrow's computational linguistics.
    Content
    Contents: Manfred Klenner / Henriette Visser: Introduction - Khurshid Ahmad: Writing Linguistics: When I use a word it means what I choose it to mean - Jürgen Handke: 2000 and Beyond: The Potential of New Technologies in Linguistics - Jurij Apresjan / Igor Boguslavsky / Leonid Iomdin / Leonid Tsinman: Lexical Functions in NU: Possible Uses - Hubert Lehmann: Practical Machine Translation and Linguistic Theory - Karin Haenelt: A Contextbased Approach towards Content Processing of Electronic Documents - Petr Sgall / Eva Hajicová: Are Linguistic Frameworks Comparable? - Wolfgang Menzel: Theory and Applications in Computational Linguistics - Is there Common Ground? - Robert Porzel / Michael Strube: Towards Context-adaptive Natural Language Processing Systems - Nicoletta Calzolari: Language Resources in a Multilingual Setting: The European Perspective - Piek Vossen: Computational Linguistics for Theory and Practice.
  14. Hutchins, J.: ¬A new era in machine translation research (1995) 0.04
    0.04103616 = product of:
      0.12310848 = sum of:
        0.05872617 = weight(_text_:applications in 3846) [ClassicSimilarity], result of:
          0.05872617 = score(doc=3846,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.34048924 = fieldWeight in 3846, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3846)
        0.014818345 = weight(_text_:of in 3846) [ClassicSimilarity], result of:
          0.014818345 = score(doc=3846,freq=8.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.24188137 = fieldWeight in 3846, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3846)
        0.049563963 = weight(_text_:systems in 3846) [ClassicSimilarity], result of:
          0.049563963 = score(doc=3846,freq=6.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.41167158 = fieldWeight in 3846, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3846)
      0.33333334 = coord(3/9)
    
    Abstract
    In the 1980s the dominant framework for machine translation research was the approach based on essentially linguistic rules. Describes the new approaches of the 1990s which are based on large text corpora, the alignment of bilingual texts, the use of statistical methods and the use of parallel corpora for example based translation. Most systems are now designed for specialized applications, such as restricted to controlled languages, to a sublanguage or to s specific domain, to a perticular organization or to a particular user type. In addition, the field is widening with research under way on speech translation, on systems for monolingual users not knowing target languages, on systems for multilingual generation directly from structured databases, and in general for uses other than those traditionally associated with translation services
  15. Working with conceptual structures : contributions to ICCS 2000. 8th International Conference on Conceptual Structures: Logical, Linguistic, and Computational Issues. Darmstadt, August 14-18, 2000 (2000) 0.04
    0.039856274 = product of:
      0.08967662 = sum of:
        0.029363085 = weight(_text_:applications in 5089) [ClassicSimilarity], result of:
          0.029363085 = score(doc=5089,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.17024462 = fieldWeight in 5089, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.02734375 = fieldNorm(doc=5089)
        0.012286724 = weight(_text_:of in 5089) [ClassicSimilarity], result of:
          0.012286724 = score(doc=5089,freq=22.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.20055744 = fieldWeight in 5089, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.02734375 = fieldNorm(doc=5089)
        0.014307884 = weight(_text_:systems in 5089) [ClassicSimilarity], result of:
          0.014307884 = score(doc=5089,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.118839346 = fieldWeight in 5089, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.02734375 = fieldNorm(doc=5089)
        0.033718925 = weight(_text_:software in 5089) [ClassicSimilarity], result of:
          0.033718925 = score(doc=5089,freq=4.0), product of:
            0.15541996 = queryWeight, product of:
              3.9671519 = idf(docFreq=2274, maxDocs=44218)
              0.03917671 = queryNorm
            0.21695362 = fieldWeight in 5089, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.9671519 = idf(docFreq=2274, maxDocs=44218)
              0.02734375 = fieldNorm(doc=5089)
      0.44444445 = coord(4/9)
    
    Abstract
    The 8th International Conference on Conceptual Structures - Logical, Linguistic, and Computational Issues (ICCS 2000) brings together a wide range of researchers and practitioners working with conceptual structures. During the last few years, the ICCS conference series has considerably widened its scope on different kinds of conceptual structures, stimulating research across domain boundaries. We hope that this stimulation is further enhanced by ICCS 2000 joining the long tradition of conferences in Darmstadt with extensive, lively discussions. This volume consists of contributions presented at ICCS 2000, complementing the volume "Conceptual Structures: Logical, Linguistic, and Computational Issues" (B. Ganter, G.W. Mineau (Eds.), LNAI 1867, Springer, Berlin-Heidelberg 2000). It contains submissions reviewed by the program committee, and position papers. We wish to express our appreciation to all the authors of submitted papers, to the general chair, the program chair, the editorial board, the program committee, and to the additional reviewers for making ICCS 2000 a valuable contribution in the knowledge processing research field. Special thanks go to the local organizers for making the conference an enjoyable and inspiring event. We are grateful to Darmstadt University of Technology, the Ernst Schröder Center for Conceptual Knowledge Processing, the Center for Interdisciplinary Studies in Technology, the Deutsche Forschungsgemeinschaft, Land Hessen, and NaviCon GmbH for their generous support
    Content
    Concepts & Language: Knowledge organization by procedures of natural language processing. A case study using the method GABEK (J. Zelger, J. Gadner) - Computer aided narrative analysis using conceptual graphs (H. Schärfe, P. 0hrstrom) - Pragmatic representation of argumentative text: a challenge for the conceptual graph approach (H. Irandoust, B. Moulin) - Conceptual graphs as a knowledge representation core in a complex language learning environment (G. Angelova, A. Nenkova, S. Boycheva, T. Nikolov) - Conceptual Modeling and Ontologies: Relationships and actions in conceptual categories (Ch. Landauer, K.L. Bellman) - Concept approximations for formal concept analysis (J. Saquer, J.S. Deogun) - Faceted information representation (U. Priß) - Simple concept graphs with universal quantifiers (J. Tappe) - A framework for comparing methods for using or reusing multiple ontologies in an application (J. van ZyI, D. Corbett) - Designing task/method knowledge-based systems with conceptual graphs (M. Leclère, F.Trichet, Ch. Choquet) - A logical ontology (J. Farkas, J. Sarbo) - Algorithms and Tools: Fast concept analysis (Ch. Lindig) - A framework for conceptual graph unification (D. Corbett) - Visual CP representation of knowledge (H.D. Pfeiffer, R.T. Hartley) - Maximal isojoin for representing software textual specifications and detecting semantic anomalies (Th. Charnois) - Troika: using grids, lattices and graphs in knowledge acquisition (H.S. Delugach, B.E. Lampkin) - Open world theorem prover for conceptual graphs (J.E. Heaton, P. Kocura) - NetCare: a practical conceptual graphs software tool (S. Polovina, D. Strang) - CGWorld - a web based workbench for conceptual graphs management and applications (P. Dobrev, K. Toutanova) - Position papers: The edition project: Peirce's existential graphs (R. Mülller) - Mining association rules using formal concept analysis (N. Pasquier) - Contextual logic summary (R Wille) - Information channels and conceptual scaling (K.E. Wolff) - Spatial concepts - a rule exploration (S. Rudolph) - The TEXT-TO-ONTO learning environment (A. Mädche, St. Staab) - Controlling the semantics of metadata on audio-visual documents using ontologies (Th. Dechilly, B. Bachimont) - Building the ontological foundations of a terminology from natural language to conceptual graphs with Ribosome, a knowledge extraction system (Ch. Jacquelinet, A. Burgun) - CharGer: some lessons learned and new directions (H.S. Delugach) - Knowledge management using conceptual graphs (W.K. Pun)
  16. Mustafa el Hadi, W.: Automatic term recognition & extraction tools : examining the new interfaces and their effective communication role in LSP discourse (1998) 0.04
    0.03982502 = product of:
      0.11947505 = sum of:
        0.050336715 = weight(_text_:applications in 67) [ClassicSimilarity], result of:
          0.050336715 = score(doc=67,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.2918479 = fieldWeight in 67, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.046875 = fieldNorm(doc=67)
        0.020082738 = weight(_text_:of in 67) [ClassicSimilarity], result of:
          0.020082738 = score(doc=67,freq=20.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.32781258 = fieldWeight in 67, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=67)
        0.0490556 = weight(_text_:systems in 67) [ClassicSimilarity], result of:
          0.0490556 = score(doc=67,freq=8.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.4074492 = fieldWeight in 67, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=67)
      0.33333334 = coord(3/9)
    
    Abstract
    In this paper we will discuss the possibility of reorienting NLP (Natural Language Processing) systems towards the extraction, not only of terms and their semantic relations, but also towards a variety of other uses; the storage, accessing and retrieving of Language for Special Purposes (LSPZ-20) lexical combinations, the provision of contexts and other information on terms through the integration of more interfaces to terminological data-bases, term managing systems and existing NLP systems. The aim of making such interfaces available is to increase the efficiency of the systems and improve the terminology-oriented text analysis. Since automatic term extraction is the backbone of many applications such as machine translation (MT), indexing, technical writing, thesaurus construction and knowledge representation developments in this area will have asignificant impact
    Source
    Structures and relations in knowledge organization: Proceedings of the 5th International ISKO-Conference, Lille, 25.-29.8.1998. Ed.: W. Mustafa el Hadi et al
  17. Stoykova, V.; Petkova, E.: Automatic extraction of mathematical terms for precalculus (2012) 0.04
    0.039748333 = product of:
      0.11924499 = sum of:
        0.05872617 = weight(_text_:applications in 156) [ClassicSimilarity], result of:
          0.05872617 = score(doc=156,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.34048924 = fieldWeight in 156, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0546875 = fieldNorm(doc=156)
        0.0128330635 = weight(_text_:of in 156) [ClassicSimilarity], result of:
          0.0128330635 = score(doc=156,freq=6.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.20947541 = fieldWeight in 156, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=156)
        0.047685754 = weight(_text_:software in 156) [ClassicSimilarity], result of:
          0.047685754 = score(doc=156,freq=2.0), product of:
            0.15541996 = queryWeight, product of:
              3.9671519 = idf(docFreq=2274, maxDocs=44218)
              0.03917671 = queryNorm
            0.30681872 = fieldWeight in 156, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.9671519 = idf(docFreq=2274, maxDocs=44218)
              0.0546875 = fieldNorm(doc=156)
      0.33333334 = coord(3/9)
    
    Abstract
    In this work, we present the results of research for evaluating a methodology for extracting mathematical terms for precalculus using the techniques for semantically-oriented statistical search. We use the corpus-based approach and the combination of different statistically-based techniques for extracting keywords, collocations and co-occurrences incorporated in the Sketch Engine software. We evaluate the collocations candidate terms for the basic concept function(s) and approve the related methodology by precalculus domain conceptual terms definitions. Finally, we offer a conceptual terms hierarchical representation and discuss the results with respect to their possible applications.
  18. Muresan, S.; Klavans, J.L.: Inducing terminologies from text : a case study for the consumer health domain (2013) 0.04
    0.0361387 = product of:
      0.108416095 = sum of:
        0.07118686 = weight(_text_:applications in 682) [ClassicSimilarity], result of:
          0.07118686 = score(doc=682,freq=4.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.41273528 = fieldWeight in 682, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.046875 = fieldNorm(doc=682)
        0.012701439 = weight(_text_:of in 682) [ClassicSimilarity], result of:
          0.012701439 = score(doc=682,freq=8.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.20732689 = fieldWeight in 682, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=682)
        0.0245278 = weight(_text_:systems in 682) [ClassicSimilarity], result of:
          0.0245278 = score(doc=682,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.2037246 = fieldWeight in 682, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=682)
      0.33333334 = coord(3/9)
    
    Abstract
    Specialized medical ontologies and terminologies, such as SNOMED CT and the Unified Medical Language System (UMLS), have been successfully leveraged in medical information systems to provide a standard web-accessible medium for interoperability, access, and reuse. However, these clinically oriented terminologies and ontologies cannot provide sufficient support when integrated into consumer-oriented applications, because these applications must "understand" both technical and lay vocabulary. The latter is not part of these specialized terminologies and ontologies. In this article, we propose a two-step approach for building consumer health terminologies from text: 1) automatic extraction of definitions from consumer-oriented articles and web documents, which reflects language in use, rather than relying solely on dictionaries, and 2) learning to map definitions expressed in natural language to terminological knowledge by inducing a syntactic-semantic grammar rather than using hand-written patterns or grammars. We present quantitative and qualitative evaluations of our two-step approach, which show that our framework could be used to induce consumer health terminologies from text.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.727-744
  19. Basili, R.; Pazienza, M.T.; Velardi, P.: ¬An empirical symbolic approach to natural language processing (1996) 0.04
    0.035760477 = product of:
      0.107281424 = sum of:
        0.06711562 = weight(_text_:applications in 6753) [ClassicSimilarity], result of:
          0.06711562 = score(doc=6753,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.38913056 = fieldWeight in 6753, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0625 = fieldNorm(doc=6753)
        0.018934188 = weight(_text_:of in 6753) [ClassicSimilarity], result of:
          0.018934188 = score(doc=6753,freq=10.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.3090647 = fieldWeight in 6753, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=6753)
        0.021231614 = product of:
          0.042463228 = sum of:
            0.042463228 = weight(_text_:22 in 6753) [ClassicSimilarity], result of:
              0.042463228 = score(doc=6753,freq=2.0), product of:
                0.13719016 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03917671 = queryNorm
                0.30952093 = fieldWeight in 6753, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=6753)
          0.5 = coord(1/2)
      0.33333334 = coord(3/9)
    
    Abstract
    Describes and evaluates the results of a large scale lexical learning system, ARISTO-LEX, that uses a combination of probabilisitc and knowledge based methods for the acquisition of selectional restrictions of words in sublanguages. Presents experimental data obtained from different corpora in different doamins and languages, and shows that the acquired lexical data not only have practical applications in natural language processing, but they are useful for a comparative analysis of sublanguages
    Date
    6. 3.1997 16:22:15
  20. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.03
    0.034636453 = product of:
      0.10390935 = sum of:
        0.05872617 = weight(_text_:applications in 267) [ClassicSimilarity], result of:
          0.05872617 = score(doc=267,freq=2.0), product of:
            0.17247584 = queryWeight, product of:
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.03917671 = queryNorm
            0.34048924 = fieldWeight in 267, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4025097 = idf(docFreq=1471, maxDocs=44218)
              0.0546875 = fieldNorm(doc=267)
        0.016567415 = weight(_text_:of in 267) [ClassicSimilarity], result of:
          0.016567415 = score(doc=267,freq=10.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.2704316 = fieldWeight in 267, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=267)
        0.028615767 = weight(_text_:systems in 267) [ClassicSimilarity], result of:
          0.028615767 = score(doc=267,freq=2.0), product of:
            0.12039685 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03917671 = queryNorm
            0.23767869 = fieldWeight in 267, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0546875 = fieldNorm(doc=267)
      0.33333334 = coord(3/9)
    
    Abstract
    Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
    Source
    Journal of Intelligent Information Systems

Languages

Types

  • a 476
  • el 62
  • m 50
  • s 22
  • x 14
  • p 7
  • d 2
  • b 1
  • n 1
  • r 1
  • More… Less…

Subjects

Classifications