Search (157 results, page 1 of 8)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.42 
    0.41826025 = product of:
  0.5019123 = sum of:
    0.060683887 = product of:
      0.18205166 = sum of:
        0.18205166 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
          0.18205166 = score(doc=562,freq=2.0), product of:
            0.32392493 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.038207654 = 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)
    0.18205166 = weight(_text_:2f in 562) [ClassicSimilarity], result of:
      0.18205166 = score(doc=562,freq=2.0), product of:
        0.32392493 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.038207654 = 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)
    0.18205166 = weight(_text_:2f in 562) [ClassicSimilarity], result of:
      0.18205166 = score(doc=562,freq=2.0), product of:
        0.32392493 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.038207654 = 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)
    0.06677184 = weight(_text_:propose in 562) [ClassicSimilarity], result of:
      0.06677184 = score(doc=562,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.3403687 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
    0.010353219 = product of:
      0.031059656 = sum of:
        0.031059656 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
          0.031059656 = score(doc=562,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = 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)
      0.33333334 = coord(1/3)
  0.8333333 = coord(5/6)
    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
  2. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.29 
    0.29415226 = product of:
  0.44122836 = sum of:
    0.18205166 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.18205166 = score(doc=563,freq=2.0), product of:
        0.32392493 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.038207654 = 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)
    0.18205166 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.18205166 = score(doc=563,freq=2.0), product of:
        0.32392493 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.038207654 = 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)
    0.06677184 = weight(_text_:propose in 563) [ClassicSimilarity], result of:
      0.06677184 = score(doc=563,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.3403687 = fieldWeight in 563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.046875 = fieldNorm(doc=563)
    0.010353219 = product of:
      0.031059656 = sum of:
        0.031059656 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
          0.031059656 = score(doc=563,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = 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)
      0.33333334 = coord(1/3)
  0.6666667 = coord(4/6)
    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
  3. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.21 
    0.21239361 = product of:
  0.42478722 = sum of:
    0.060683887 = product of:
      0.18205166 = sum of:
        0.18205166 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.18205166 = score(doc=862,freq=2.0), product of:
            0.32392493 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.038207654 = 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)
    0.18205166 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
      0.18205166 = score(doc=862,freq=2.0), product of:
        0.32392493 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.038207654 = 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)
    0.18205166 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
      0.18205166 = score(doc=862,freq=2.0), product of:
        0.32392493 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.038207654 = 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)
  0.5 = coord(3/6)
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  4. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.05 
    0.04686617 = product of:
  0.1405985 = sum of:
    0.119892076 = weight(_text_:forschung in 4888) [ClassicSimilarity], result of:
      0.119892076 = score(doc=4888,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.64500517 = fieldWeight in 4888, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.09375 = fieldNorm(doc=4888)
    0.020706438 = product of:
      0.062119313 = sum of:
        0.062119313 = weight(_text_:22 in 4888) [ClassicSimilarity], result of:
          0.062119313 = score(doc=4888,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = queryNorm
            0.46428138 = fieldWeight in 4888, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=4888)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    Mit einem Überblick über: Probleme, Methoden, Stand der Forschung u. Literatur.
    Date
    1. 3.2013 14:56:22
  5. Zhang, C.; Zeng, D.; Li, J.; Wang, F.-Y.; Zuo, W.: Sentiment analysis of Chinese documents : from sentence to document level (2009) 0.03 
    0.025739681 = product of:
  0.07721904 = sum of:
    0.06677184 = weight(_text_:propose in 3296) [ClassicSimilarity], result of:
      0.06677184 = score(doc=3296,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.3403687 = fieldWeight in 3296, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.046875 = fieldNorm(doc=3296)
    0.0104471985 = product of:
      0.031341594 = sum of:
        0.031341594 = weight(_text_:29 in 3296) [ClassicSimilarity], result of:
          0.031341594 = score(doc=3296,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.23319192 = fieldWeight in 3296, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=3296)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    User-generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule-based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning-based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches.
    Date
    2. 2.2010 19:29:56
  6. Wahlster, W.: Verbmobil : Erkennung, Analyse, Transfer, Generierung und Synthese von Spontansprache (2001) 0.02 
    0.023464411 = product of:
  0.070393234 = sum of:
    0.059946038 = weight(_text_:forschung in 5629) [ClassicSimilarity], result of:
      0.059946038 = score(doc=5629,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.32250258 = fieldWeight in 5629, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.046875 = fieldNorm(doc=5629)
    0.0104471985 = product of:
      0.031341594 = sum of:
        0.031341594 = weight(_text_:29 in 5629) [ClassicSimilarity], result of:
          0.031341594 = score(doc=5629,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.23319192 = fieldWeight in 5629, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=5629)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    Verbmobil ist ein langfristig angelegtes, interdisziplinäres Leitprojekt im Bereich der Sprachtechnologie. Das Verbmobil-System erkennt gesprochene Spontansprache, analysiert die Eingabe, übersetzt sie in eine Fremdsprache, erzeugt einen Satz und spricht ihn aus. Für ausgewählte Themenbereiche (z.B. Terminverhandlung, Reiseplanung, Fernwartung) soll Verbmobil Übersetzungshilfe in Gesprächssituationen mit ausländischen Partnern leisten. Das Verbundvorhaben, in dem Unternehmen der Informationstechnologie, Universitäten und Forschungszentren kooperieren, wird vom Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (BMBF) in zwei Phasen (Laufzeit Phase 1: 1993-1996; Phase 2: 1997 - 2000) gefördert. Nachdem in der ersten Phase Terminverhandlungsdialoge zwischen einem deutschen und japanischen Geschäftspartner mit Englisch als Zwischensprache verarbeitet wurden, steht in der zweiten Phase von Verbmobil die robuste und bidirektionale Übersetzung spontansprachlicher Dialoge aus den Domänen Reiseplanung und Hotelreservierung für die Sprachpaare Deutsch-Englisch (ca. 10. 000 Wörter) und Deutsch-Japanisch (ca. 2.500 Wörter) im Vordergrund
    Date
    29. 1.1997 18:49:05
  7. Li, W.; Wong, K.-F.; Yuan, C.: Toward automatic Chinese temporal information extraction (2001) 0.02 
    0.021449735 = product of:
  0.064349204 = sum of:
    0.055643205 = weight(_text_:propose in 6029) [ClassicSimilarity], result of:
      0.055643205 = score(doc=6029,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.2836406 = fieldWeight in 6029, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6029)
    0.008706 = product of:
      0.026117997 = sum of:
        0.026117997 = weight(_text_:29 in 6029) [ClassicSimilarity], result of:
          0.026117997 = score(doc=6029,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.19432661 = fieldWeight in 6029, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6029)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    Over the past few years, temporal information processing and temporal database management have increasingly become hot topics. Nevertheless, only a few researchers have investigated these areas in the Chinese language. This lays down the objective of our research: to exploit Chinese language processing techniques for temporal information extraction and concept reasoning. In this article, we first study the mechanism for expressing time in Chinese. On the basis of the study, we then design a general frame structure for maintaining the extracted temporal concepts and propose a system for extracting time-dependent information from Hong Kong financial news. In the system, temporal knowledge is represented by different types of temporal concepts (TTC) and different temporal relations, including absolute and relative relations, which are used to correlate between action times and reference times. In analyzing a sentence, the algorithm first determines the situation related to the verb. This in turn will identify the type of temporal concept associated with the verb. After that, the relevant temporal information is extracted and the temporal relations are derived. These relations link relevant concept frames together in chronological order, which in turn provide the knowledge to fulfill users' queries, e.g., for question-answering (i.e., Q&A) applications
    Date
    29. 9.2001 14:02:50
  8. Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002) 0.02 
    0.021449735 = product of:
  0.064349204 = sum of:
    0.055643205 = weight(_text_:propose in 1853) [ClassicSimilarity], result of:
      0.055643205 = score(doc=1853,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.2836406 = fieldWeight in 1853, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1853)
    0.008706 = product of:
      0.026117997 = sum of:
        0.026117997 = weight(_text_:29 in 1853) [ClassicSimilarity], result of:
          0.026117997 = score(doc=1853,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.19432661 = fieldWeight in 1853, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1853)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    In a scientific and technological watch (STW) task, an expert user needs to survey the evolution of research topics in his area of specialisation in order to detect interesting changes. The majority of methods proposing evaluation metrics (bibliometrics and scientometrics studies) for STW rely solely an statistical data analysis methods (Co-citation analysis, co-word analysis). Such methods usually work an structured databases where the units of analysis (words, keywords) are already attributed to documents by human indexers. The advent of huge amounts of unstructured textual data has rendered necessary the integration of natural language processing (NLP) techniques to first extract meaningful units from texts. We propose a method for STW which is NLP-oriented. The method not only analyses texts linguistically in order to extract terms from them, but also uses linguistic relations (syntactic variations) as the basis for clustering. Terms and variation relations are formalised as weighted di-graphs which the clustering algorithm, CPCL (Classification by Preferential Clustered Link) will seek to reduce in order to produces classes. These classes ideally represent the research topics present in the corpus. The results of the classification are subjected to validation by an expert in STW.
    Source
    Knowledge organization. 29(2002) nos.3/4, S.181-197
  9. Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017) 0.02 
    0.021449735 = product of:
  0.064349204 = sum of:
    0.055643205 = weight(_text_:propose in 3682) [ClassicSimilarity], result of:
      0.055643205 = score(doc=3682,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.2836406 = fieldWeight in 3682, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3682)
    0.008706 = product of:
      0.026117997 = sum of:
        0.026117997 = weight(_text_:29 in 3682) [ClassicSimilarity], result of:
          0.026117997 = score(doc=3682,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.19432661 = fieldWeight in 3682, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3682)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    Analyzing large quantities of real-world textual data has the potential to provide new insights for researchers. However, such data present challenges for both human and computational methods, requiring a diverse range of specialist skills, often shared across a number of individuals. In this paper we use the analysis of a real-world data set as our case study, and use this exploration as a demonstration of our "insight workflow," which we present for use and adaptation by other researchers. The data we use are impact case study documents collected as part of the UK Research Excellence Framework (REF), consisting of 6,679 documents and 6.25 million words; the analysis was commissioned by the Higher Education Funding Council for England (published as report HEFCE 2015). In our exploration and analysis we used a variety of techniques, ranging from keyword in context and frequency information to more sophisticated methods (topic modeling), with these automated techniques providing an empirical point of entry for in-depth and intensive human analysis. We present the 60 topics to demonstrate the output of our methods, and illustrate how the variety of analysis techniques can be combined to provide insights. We note potential limitations and propose future work.
    Date
    16.11.2017 14:00:29
  10. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.02 
    0.021423629 = product of:
  0.064270884 = sum of:
    0.055643205 = weight(_text_:propose in 1171) [ClassicSimilarity], result of:
      0.055643205 = score(doc=1171,freq=2.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.2836406 = fieldWeight in 1171, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1171)
    0.008627683 = product of:
      0.025883049 = sum of:
        0.025883049 = weight(_text_:22 in 1171) [ClassicSimilarity], result of:
          0.025883049 = score(doc=1171,freq=2.0), product of:
            0.13379669 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038207654 = queryNorm
            0.19345059 = fieldWeight in 1171, 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=1171)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    Logical rules are essential for uncovering the logical connections between relations, which could improve the reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from the computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, a rule validator harnesses the reasoning ability of LLMs to validate the logical correctness of ranked rules through chain-of-thought reasoning. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.
    Date
    23.11.2023 19:07:22
  11. Konrad, K.; Maier, H.; Pinkal, M.; Milward, D.: CLEARS: ein Werkzeug für Ausbildung und Forschung in der Computerlinguistik (1996) 0.02 
    0.019982014 = product of:
  0.119892076 = sum of:
    0.119892076 = weight(_text_:forschung in 7298) [ClassicSimilarity], result of:
      0.119892076 = score(doc=7298,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.64500517 = fieldWeight in 7298, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.09375 = fieldNorm(doc=7298)
  0.16666667 = coord(1/6)
  12. Wilhelm, K.: ¬Die Macht der Grammatik (2000) 0.02 
    0.016651679 = product of:
  0.099910066 = sum of:
    0.099910066 = weight(_text_:forschung in 5510) [ClassicSimilarity], result of:
      0.099910066 = score(doc=5510,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.5375043 = fieldWeight in 5510, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.078125 = fieldNorm(doc=5510)
  0.16666667 = coord(1/6)
    Source
    Max Planck Forschung. 2000, H.1, S.26-32
  13. Wolfangel, E.: Ich verstehe (2017) 0.02 
    0.016651679 = product of:
  0.099910066 = sum of:
    0.099910066 = weight(_text_:forschung in 3976) [ClassicSimilarity], result of:
      0.099910066 = score(doc=3976,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.5375043 = fieldWeight in 3976, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.078125 = fieldNorm(doc=3976)
  0.16666667 = coord(1/6)
    Series
    Wissen: Technik, Forschung, Umwelt, Mensch
  14. Siepmann, D.: Auswirkungen von KI auf die Textproduktion in der Wissenschaft (2023) 0.02 
    0.016651679 = product of:
  0.099910066 = sum of:
    0.099910066 = weight(_text_:forschung in 1044) [ClassicSimilarity], result of:
      0.099910066 = score(doc=1044,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.5375043 = fieldWeight in 1044, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.078125 = fieldNorm(doc=1044)
  0.16666667 = coord(1/6)
    Source
    Forschung und Lehre [https://www.forschung-und-lehre.de/zeitfragen/welche-auswirkungen-kis-auf-die-textproduktion-in-der-wissenschaft-haben-5740]
  15. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023) 0.02 
    0.016651679 = product of:
  0.099910066 = sum of:
    0.099910066 = weight(_text_:forschung in 1051) [ClassicSimilarity], result of:
      0.099910066 = score(doc=1051,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.5375043 = fieldWeight in 1051, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.078125 = fieldNorm(doc=1051)
  0.16666667 = coord(1/6)
    Content
    Wiederabdruck des Beitrags aus: Forschung & Lehre vom 20.12.2022. Vgl.: DOI 10.1515/dmvm-2023-0007.
  16. Fang, L.; Tuan, L.A.; Hui, S.C.; Wu, L.: Syntactic based approach for grammar question retrieval (2018) 0.02 
    0.016062811 = product of:
  0.096376866 = sum of:
    0.096376866 = weight(_text_:propose in 5086) [ClassicSimilarity], result of:
      0.096376866 = score(doc=5086,freq=6.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.49127996 = fieldWeight in 5086, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5086)
  0.16666667 = coord(1/6)
    Abstract
    With the popularity of online educational platforms, English learners can learn and practice no matter where they are and what they do. English grammar is one of the important components in learning English. To learn English grammar effectively, it requires students to practice questions containing focused grammar knowledge. In this paper, we study a novel problem of retrieving English grammar questions with similar grammatical focus. Since the grammatical focus similarity is different from textual similarity or sentence syntactic similarity, existing approaches cannot be applied directly to our problem. To address this problem, we propose a syntactic based approach for English grammar question retrieval which can retrieve related grammar questions with similar grammatical focus effectively. In the proposed syntactic based approach, we first propose a new syntactic tree, namely parse-key tree, to capture English grammar questions' grammatical focus. Next, we propose two kernel functions, namely relaxed tree kernel and part-of-speech order kernel, to compute the similarity between two parse-key trees of the query and grammar questions in the collection. Then, the retrieved grammar questions are ranked according to the similarity between the parse-key trees. In addition, if a query is submitted together with answer choices, conceptual similarity and textual similarity are also incorporated to further improve the retrieval accuracy. The performance results have shown that our proposed approach outperforms the state-of-the-art methods based on statistical analysis and syntactic analysis.
  17. Nait-Baha, L.; Jackiewicz, A.; Djioua, B.; Laublet, P.: Query reformulation for information retrieval on the Web using the point of view methodology : preliminary results (2001) 0.02 
    0.015738275 = product of:
  0.09442965 = sum of:
    0.09442965 = weight(_text_:propose in 249) [ClassicSimilarity], result of:
      0.09442965 = score(doc=249,freq=4.0), product of:
        0.19617504 = queryWeight, product of:
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.038207654 = queryNorm
        0.48135406 = fieldWeight in 249, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.1344433 = idf(docFreq=707, maxDocs=44218)
          0.046875 = fieldNorm(doc=249)
  0.16666667 = coord(1/6)
    Abstract
    The work we are presenting is devoted to the information collected on the WWW. By the term collected we mean the whole process of retrieving, extracting and presenting results to the user. This research is part of the RAP (Research, Analyze, Propose) project in which we propose to combine two methods: (i) query reformulation using linguistic markers according to a given point of view; and (ii) text semantic analysis by means of contextual exploration results (Descles, 1991). The general project architecture describing the interactions between the users, the RAP system and the WWW search engines is presented in Nait-Baha et al. (1998). We will focus this paper on showing how we use linguistic markers to reformulate the queries according to a given point of view
  18. Donath, A.: Nutzungsverbote für ChatGPT (2023) 0.02 
    0.015642941 = product of:
  0.046928823 = sum of:
    0.039964024 = weight(_text_:forschung in 877) [ClassicSimilarity], result of:
      0.039964024 = score(doc=877,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.21500172 = fieldWeight in 877, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.03125 = fieldNorm(doc=877)
    0.006964799 = product of:
      0.020894397 = sum of:
        0.020894397 = weight(_text_:29 in 877) [ClassicSimilarity], result of:
          0.020894397 = score(doc=877,freq=2.0), product of:
            0.13440257 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.038207654 = queryNorm
            0.15546128 = fieldWeight in 877, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.03125 = fieldNorm(doc=877)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Content
    Milliardenbewertung für ChatGPT OpenAI, das Chatbot ChatGPT betreibt, befindet sich laut einem Bericht des Wall Street Journals in Gesprächen zu einem Aktienverkauf. Das WSJ meldete, der mögliche Verkauf der Aktien würde die Bewertung von OpenAI auf 29 Milliarden US-Dollar anheben. Sorgen auch in Brandenburg Der brandenburgische SPD-Abgeordnete Erik Stohn stellte mit Hilfe von ChatGPT eine Kleine Anfrage an den Brandenburger Landtag, in der er fragte, wie die Landesregierung sicherstelle, dass Studierende bei maschinell erstellten Texten gerecht beurteilt und benotet würden. Er fragte auch nach Maßnahmen, die ergriffen worden seien, um sicherzustellen, dass maschinell erstellte Texte nicht in betrügerischer Weise von Studierenden bei der Bewertung von Studienleistungen verwendet werden könnten.
    Der Autor meint dazu Es ist verständlich, dass sich Lehrer und Wissenschaftler Gedanken darüber machen, wie die Verwendung von künstlicher Intelligenz in der Bildung nicht zu negativen Effekten führt. Es ist wichtig, dass Schüler fair beurteilt werden und niemand Vorteile aus einem Betrug hat. Gleichzeitig ist es jedoch auch wichtig, dass Schüler und Wissenschaftler die Möglichkeit haben, Technologien und Tools zu nutzen, die ihnen helfen können, ihr volles Potential auszuschöpfen. Es wird interessant sein, zu sehen, welche Maßnahmen ergriffen werden, um sicherzustellen, dass die Verwendung von KI in der Bildung und Forschung fair und sicher ist."
  19. Heid, U.: Computerlinguistik zwischen Informationswissenschaft und multilingualer Kommunikation (2010) 0.01 
    0.013321342 = product of:
  0.07992805 = sum of:
    0.07992805 = weight(_text_:forschung in 4018) [ClassicSimilarity], result of:
      0.07992805 = score(doc=4018,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.43000343 = fieldWeight in 4018, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.0625 = fieldNorm(doc=4018)
  0.16666667 = coord(1/6)
    Abstract
    Dieser Beitrag widmet sich den Möglichkeiten der Interaktion zwischen Informationswissenschaft und Computerlinguistik. Dazu werden relevante Aspekte computerlinguistischer Forschung präsentiert, und ihr Potential für die Interaktion mit informationswissenschaftlichen Fragestellungen und Produkten wird erläutert. Im dritten Teil werden anhand der spezifischen Hildesheimer Situation Vorschläge für eine solche Interaktion diskutiert, und zwar im Dreieck: Informationswissenschaft, Computerlinguistik und Multilinguale Kommunikation.
  20. Ludwig, B.; Reischer, J.: Informationslinguistik in Regensburg (2012) 0.01 
    0.013321342 = product of:
  0.07992805 = sum of:
    0.07992805 = weight(_text_:forschung in 555) [ClassicSimilarity], result of:
      0.07992805 = score(doc=555,freq=2.0), product of:
        0.1858777 = queryWeight, product of:
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.038207654 = queryNorm
        0.43000343 = fieldWeight in 555, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8649335 = idf(docFreq=926, maxDocs=44218)
          0.0625 = fieldNorm(doc=555)
  0.16666667 = coord(1/6)
    Abstract
    In ihrem Beitrag stellen die Autoren die Informationslinguistik als Teildisziplin der Informationswissenschaft vor, grenzen sie gegen benachbarte Fächer Theoretische Linguistik, Computerlinguistik und Maschinelle Sprachverarbeitung ab, zeigen aber zugleich auch Gemeinsamkeiten und Überschneidungsbereiche auf. Anwendungsbereiche, Verfahren und Produkte der Informationslinguistik werden in einem kurzen Überblick eingeführt. Einige davon, die im Zentrum der Forschung an der Universität Regensburg stehen, werden unter Bezugnahme auf aktuelle Arbeiten und Forschungsprojekte näher erläutert.

Authors

Years

Languages

  • e 105
  • d 46
  • ru 2
  • m 1
  • More… Less…

Types

  • a 133
  • el 17
  • m 12
  • s 6
  • x 4
  • p 2
  • d 1
  • More… Less…

Themes

Subjects

Classifications