Search (89 results, page 1 of 5)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.09
    0.08973381 = product of:
      0.26920143 = sum of:
        0.22996698 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
          0.22996698 = score(doc=562,freq=2.0), product of:
            0.4091808 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.04826377 = 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)
        0.03923445 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
          0.03923445 = score(doc=562,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = 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(2/6)
    
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04
    0.03832783 = product of:
      0.22996698 = sum of:
        0.22996698 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.22996698 = score(doc=862,freq=2.0), product of:
            0.4091808 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.04826377 = 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)
      0.16666667 = coord(1/6)
    
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Dorr, B.J.: Large-scale dictionary construction for foreign language tutoring and interlingual machine translation (1997) 0.03
    0.032291643 = product of:
      0.09687492 = sum of:
        0.05764047 = weight(_text_:problem in 3244) [ClassicSimilarity], result of:
          0.05764047 = score(doc=3244,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.28137225 = fieldWeight in 3244, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=3244)
        0.03923445 = weight(_text_:22 in 3244) [ClassicSimilarity], result of:
          0.03923445 = score(doc=3244,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.23214069 = fieldWeight in 3244, 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=3244)
      0.33333334 = coord(2/6)
    
    Abstract
    Describes techniques for automatic construction of dictionaries for use in large-scale foreign language tutoring (FLT) and interlingual machine translation (MT) systems. The dictionaries are based on a language independent representation called lexical conceptual structure (LCS). Demonstrates that synonymous verb senses share distribution patterns. Shows how the syntax-semantics relation can be used to develop a lexical acquisition approach that contributes both toward the enrichment of existing online resources and toward the development of lexicons containing more complete information than is provided in any of these resources alone. Describes the structure of the LCS and shows how this representation is used in FLT and MT. Focuses on the problem of building LCS dictionaries for large-scale FLT and MT. Describes authoring tools for manual and semi-automatic construction of LCS dictionaries. Presents an approach that uses linguistic techniques for building word definitions automatically. The techniques have been implemented as part of a set of lixicon-development tools used in the MILT FLT project
    Date
    31. 7.1996 9:22:19
  4. Bian, G.-W.; Chen, H.-H.: Cross-language information access to multilingual collections on the Internet (2000) 0.03
    0.032291643 = product of:
      0.09687492 = sum of:
        0.05764047 = weight(_text_:problem in 4436) [ClassicSimilarity], result of:
          0.05764047 = score(doc=4436,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.28137225 = fieldWeight in 4436, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=4436)
        0.03923445 = weight(_text_:22 in 4436) [ClassicSimilarity], result of:
          0.03923445 = score(doc=4436,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.23214069 = fieldWeight in 4436, 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=4436)
      0.33333334 = coord(2/6)
    
    Abstract
    Language barrier is the major problem that people face in searching for, retrieving, and understanding multilingual collections on the Internet. This paper deals with query translation and document translation in a Chinese-English information retrieval system called MTIR. Bilingual dictionary and monolingual corpus-based approaches are adopted to select suitable tranlated query terms. A machine transliteration algorithm is introduced to resolve proper name searching. We consider several design issues for document translation, including which material is translated, what roles the HTML tags play in translation, what the tradeoff is between the speed performance and the translation performance, and what from the translated result is presented in. About 100.000 Web pages translated in the last 4 months of 1997 are used for quantitative study of online and real-time Web page translation
    Date
    16. 2.2000 14:22:39
  5. Metz, C.: ¬The new chatbots could change the world : can you trust them? (2022) 0.02
    0.01921349 = product of:
      0.11528094 = sum of:
        0.11528094 = weight(_text_:problem in 854) [ClassicSimilarity], result of:
          0.11528094 = score(doc=854,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.5627445 = fieldWeight in 854, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.09375 = fieldNorm(doc=854)
      0.16666667 = coord(1/6)
    
    Abstract
    Siri, Google Search, online marketing and your child's homework will never be the same. Then there's the misinformation problem.
  6. Yang, C.C.; Luk, J.: Automatic generation of English/Chinese thesaurus based on a parallel corpus in laws (2003) 0.02
    0.018836789 = product of:
      0.056510366 = sum of:
        0.033623606 = weight(_text_:problem in 1616) [ClassicSimilarity], result of:
          0.033623606 = score(doc=1616,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.1641338 = fieldWeight in 1616, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1616)
        0.02288676 = weight(_text_:22 in 1616) [ClassicSimilarity], result of:
          0.02288676 = score(doc=1616,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.1354154 = fieldWeight in 1616, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1616)
      0.33333334 = coord(2/6)
    
    Abstract
    The information available in languages other than English in the World Wide Web is increasing significantly. According to a report from Computer Economics in 1999, 54% of Internet users are English speakers ("English Will Dominate Web for Only Three More Years," Computer Economics, July 9, 1999, http://www.computereconomics. com/new4/pr/pr990610.html). However, it is predicted that there will be only 60% increase in Internet users among English speakers verses a 150% growth among nonEnglish speakers for the next five years. By 2005, 57% of Internet users will be non-English speakers. A report by CNN.com in 2000 showed that the number of Internet users in China had been increased from 8.9 million to 16.9 million from January to June in 2000 ("Report: China Internet users double to 17 million," CNN.com, July, 2000, http://cnn.org/2000/TECH/computing/07/27/ china.internet.reut/index.html). According to Nielsen/ NetRatings, there was a dramatic leap from 22.5 millions to 56.6 millions Internet users from 2001 to 2002. China had become the second largest global at-home Internet population in 2002 (US's Internet population was 166 millions) (Robyn Greenspan, "China Pulls Ahead of Japan," Internet.com, April 22, 2002, http://cyberatias.internet.com/big-picture/geographics/article/0,,5911_1013841,00. html). All of the evidences reveal the importance of crosslingual research to satisfy the needs in the near future. Digital library research has been focusing in structural and semantic interoperability in the past. Searching and retrieving objects across variations in protocols, formats and disciplines are widely explored (Schatz, B., & Chen, H. (1999). Digital libraries: technological advances and social impacts. IEEE Computer, Special Issue an Digital Libraries, February, 32(2), 45-50.; Chen, H., Yen, J., & Yang, C.C. (1999). International activities: development of Asian digital libraries. IEEE Computer, Special Issue an Digital Libraries, 32(2), 48-49.). However, research in crossing language boundaries, especially across European languages and Oriental languages, is still in the initial stage. In this proposal, we put our focus an cross-lingual semantic interoperability by developing automatic generation of a cross-lingual thesaurus based an English/Chinese parallel corpus. When the searchers encounter retrieval problems, Professional librarians usually consult the thesaurus to identify other relevant vocabularies. In the problem of searching across language boundaries, a cross-lingual thesaurus, which is generated by co-occurrence analysis and Hopfield network, can be used to generate additional semantically relevant terms that cannot be obtained from dictionary. In particular, the automatically generated cross-lingual thesaurus is able to capture the unknown words that do not exist in a dictionary, such as names of persons, organizations, and events. Due to Hong Kong's unique history background, both English and Chinese are used as official languages in all legal documents. Therefore, English/Chinese cross-lingual information retrieval is critical for applications in courts and the government. In this paper, we develop an automatic thesaurus by the Hopfield network based an a parallel corpus collected from the Web site of the Department of Justice of the Hong Kong Special Administrative Region (HKSAR) Government. Experiments are conducted to measure the precision and recall of the automatic generated English/Chinese thesaurus. The result Shows that such thesaurus is a promising tool to retrieve relevant terms, especially in the language that is not the same as the input term. The direct translation of the input term can also be retrieved in most of the cases.
  7. Melzer, C.: ¬Der Maschine anpassen : PC-Spracherkennung - Programme sind mittlerweile alltagsreif (2005) 0.02
    0.018836789 = product of:
      0.056510366 = sum of:
        0.033623606 = weight(_text_:problem in 4044) [ClassicSimilarity], result of:
          0.033623606 = score(doc=4044,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.1641338 = fieldWeight in 4044, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4044)
        0.02288676 = weight(_text_:22 in 4044) [ClassicSimilarity], result of:
          0.02288676 = score(doc=4044,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.1354154 = fieldWeight in 4044, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4044)
      0.33333334 = coord(2/6)
    
    Content
    Billiger geht es mit "Via Voice Standard" von IBM. Die Software kostet etwa 50 Euro, hat aber erhebliche Schwächen in der Lernfähigkeit: Sie schneidet jedoch immer noch besser ab als das gut drei Mal so teure "Voice Office Premium 10"; das im Test der sechs Programme als einziges nur ein "Befriedigend" bekam. "Man liest über Spracherkennung nicht mehr so viel" weil es funktioniert", glaubt Dorothee Wiegand von der in Hannover erscheinenden Computerzeitschrift "c't". Die Technik" etwa "Dragon Naturally Speaking" von ScanSoft, sei ausgereift, "Spracherkennung ist vor allem Statistik, die Auswertung unendlicher Wortmöglichkeiten. Eigentlich war eher die Hardware das Problem", sagt Wiegand. Da jetzt selbst einfache Heimcomputer schnell und leistungsfähig seien, hätten die Entwickler viel mehr Möglichkeiten."Aber selbst ältere Computer kommen mit den Systemen klar. Sie brauchen nur etwas länger! "Jedes Byte macht die Spracherkennung etwas schneller, ungenauer ist sie sonst aber nicht", bestätigt Kristina Henry von linguatec in München. Auch für die Produkte des Herstellers gelte jedoch, dass "üben und deutlich sprechen wichtiger sind als jede Hardware". Selbst Stimmen von Diktiergeräten würden klar, erkannt, versichert Henry: "Wir wollen einen Schritt weiter gehen und das Diktieren von unterwegs möglich machen." Der Benutzer könnte dann eine Nummer anwählen, etwa im Auto einen Text aufsprechen und ihn zu Hause "getippt" vorfinden. Grundsätzlich passt die Spracherkennungssoftware inzwischen auch auf den privaten Computer. Klar ist aber, dass selbst der bestgesprochene Text nachbearbeitet werden muss. Zudem ist vom Nutzer Geduld gefragt: Ebenso wie sein System lernt, muss der Mensch sich in Aussprache und Geschwindigkeit dem System anpassen. Dann sind die Ergebnisse allerdings beachtlich - und "Sexterminvereinbarung" statt "zwecks Terminvereinbarung" gehört der Vergangenheit an."
    Date
    3. 5.1997 8:44:22
  8. Warner, A.J.: Natural language processing (1987) 0.02
    0.017437533 = product of:
      0.104625195 = sum of:
        0.104625195 = weight(_text_:22 in 337) [ClassicSimilarity], result of:
          0.104625195 = score(doc=337,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.61904186 = fieldWeight in 337, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.125 = fieldNorm(doc=337)
      0.16666667 = coord(1/6)
    
    Source
    Annual review of information science and technology. 22(1987), S.79-108
  9. Winograd, T.: Software für Sprachverarbeitung (1984) 0.02
    0.016011242 = product of:
      0.09606744 = sum of:
        0.09606744 = weight(_text_:problem in 1687) [ClassicSimilarity], result of:
          0.09606744 = score(doc=1687,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.46895373 = fieldWeight in 1687, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.078125 = fieldNorm(doc=1687)
      0.16666667 = coord(1/6)
    
    Abstract
    Der Computer kann mit sprachlichen Zeichen sicher und schnell umgehen. Dies zeigen Programme zur Textverarbeitung. Versuche allerdings, ihn auch mit Bedeutungen operieren zu lassen, sind gescheitert. Wird der Rechner das größte Problem der Sprachverarbeitung - die Mehrdeutigkeit natürlicher Sprachen - jemals bewältigen?
  10. Gerstenkorn, A.: Indexierung mit Nominalgruppen (1980) 0.02
    0.016011242 = product of:
      0.09606744 = sum of:
        0.09606744 = weight(_text_:problem in 6685) [ClassicSimilarity], result of:
          0.09606744 = score(doc=6685,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.46895373 = fieldWeight in 6685, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.078125 = fieldNorm(doc=6685)
      0.16666667 = coord(1/6)
    
    Abstract
    Die Indexierung mit Nominalgruppen ist eine konsequente Fortsetzung der Entwicklung von der gleichordnenden zur syntaktischen Indexierung. Nominalgruppen eignen sich besonders zur Bezeichnung komplexer Begriffe (Themen) und sind benutzerfreundlich. Bei einer automatischen Indexierung mit Nominalgruppen sind keine vollständigen Satzanalysen nötig, auch Systeme mit einem partiellen Parser liefern brauchbare Ergebnisse. Das Problem eines Retrieval mit Nominalgruppen ist noch zu lösen
  11. Wenzel, F.: Semantische Eingrenzung im Freitext-Retrieval auf der Basis morphologischer Segmentierungen (1980) 0.02
    0.016011242 = product of:
      0.09606744 = sum of:
        0.09606744 = weight(_text_:problem in 2037) [ClassicSimilarity], result of:
          0.09606744 = score(doc=2037,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.46895373 = fieldWeight in 2037, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.078125 = fieldNorm(doc=2037)
      0.16666667 = coord(1/6)
    
    Abstract
    The basic problem in freetext retrieval is that the retrieval language is not properly adapted to that of the author. Morphological segmentation, where words with the same root are grouped together in the inverted file, is a good eliminator of noise and information loss, providing high recall but low precision
  12. Pimenov, E.N.: Normativnost' i nekotorye problem razrabotki tezauruzov i drugikh lingvistiicheskikh sredstv IPS (2000) 0.02
    0.016011242 = product of:
      0.09606744 = sum of:
        0.09606744 = weight(_text_:problem in 3281) [ClassicSimilarity], result of:
          0.09606744 = score(doc=3281,freq=2.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.46895373 = fieldWeight in 3281, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.078125 = fieldNorm(doc=3281)
      0.16666667 = coord(1/6)
    
  13. McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.02
    0.015257841 = product of:
      0.09154704 = sum of:
        0.09154704 = weight(_text_:22 in 3164) [ClassicSimilarity], result of:
          0.09154704 = score(doc=3164,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.5416616 = fieldWeight in 3164, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=3164)
      0.16666667 = coord(1/6)
    
    Source
    Computational linguistics. 22(1996) no.2, S.217-248
  14. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.02
    0.015257841 = product of:
      0.09154704 = sum of:
        0.09154704 = weight(_text_:22 in 4506) [ClassicSimilarity], result of:
          0.09154704 = score(doc=4506,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.5416616 = fieldWeight in 4506, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=4506)
      0.16666667 = coord(1/6)
    
    Date
    8.10.2000 11:52:22
  15. Somers, H.: Example-based machine translation : Review article (1999) 0.02
    0.015257841 = product of:
      0.09154704 = sum of:
        0.09154704 = weight(_text_:22 in 6672) [ClassicSimilarity], result of:
          0.09154704 = score(doc=6672,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.5416616 = fieldWeight in 6672, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=6672)
      0.16666667 = coord(1/6)
    
    Date
    31. 7.1996 9:22:19
  16. New tools for human translators (1997) 0.02
    0.015257841 = product of:
      0.09154704 = sum of:
        0.09154704 = weight(_text_:22 in 1179) [ClassicSimilarity], result of:
          0.09154704 = score(doc=1179,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.5416616 = fieldWeight in 1179, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=1179)
      0.16666667 = coord(1/6)
    
    Date
    31. 7.1996 9:22:19
  17. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.02
    0.015257841 = product of:
      0.09154704 = sum of:
        0.09154704 = weight(_text_:22 in 3117) [ClassicSimilarity], result of:
          0.09154704 = score(doc=3117,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.5416616 = fieldWeight in 3117, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=3117)
      0.16666667 = coord(1/6)
    
    Date
    28. 2.1999 10:48:22
  18. ¬Der Student aus dem Computer (2023) 0.02
    0.015257841 = product of:
      0.09154704 = sum of:
        0.09154704 = weight(_text_:22 in 1079) [ClassicSimilarity], result of:
          0.09154704 = score(doc=1079,freq=2.0), product of:
            0.1690115 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04826377 = queryNorm
            0.5416616 = fieldWeight in 1079, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=1079)
      0.16666667 = coord(1/6)
    
    Date
    27. 1.2023 16:22:55
  19. Fang, L.; Tuan, L.A.; Hui, S.C.; Wu, L.: Syntactic based approach for grammar question retrieval (2018) 0.01
    0.013866143 = product of:
      0.083196856 = sum of:
        0.083196856 = weight(_text_:problem in 5086) [ClassicSimilarity], result of:
          0.083196856 = score(doc=5086,freq=6.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.4061259 = fieldWeight in 5086, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.244485 = idf(docFreq=1723, 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.
  20. Rahmstorf, G.: Information retrieval using conceptual representations of phrases (1994) 0.01
    0.01358599 = product of:
      0.08151594 = sum of:
        0.08151594 = weight(_text_:problem in 7862) [ClassicSimilarity], result of:
          0.08151594 = score(doc=7862,freq=4.0), product of:
            0.20485485 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04826377 = queryNorm
            0.39792046 = fieldWeight in 7862, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=7862)
      0.16666667 = coord(1/6)
    
    Abstract
    The information retrieval problem is described starting from an analysis of the concepts 'user's information request' and 'information offerings of texts'. It is shown that natural language phrases are a more adequate medium for expressing information requests and information offerings than character string based query and indexing languages complemented by Boolean oprators. The phrases must be represented as concepts to reach a language invariant level for rule based relevance analysis. The special type of representation called advanced thesaurus is used for the semantic representation of natural language phrases and for relevance processing. The analysis of the retrieval problem leads to a symmetric system structure

Years

Languages

  • e 62
  • d 26
  • ru 1
  • More… Less…

Types

  • a 72
  • el 10
  • m 7
  • x 4
  • s 3
  • p 2
  • d 1
  • More… Less…

Classifications