Search (491 results, page 24 of 25)

  • × theme_ss:"Computerlinguistik"
  1. Geißler, S.: Maschinelles Lernen und NLP : Reif für die industrielle Anwendung! (2019) 0.00 
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    Source
    Information - Wissenschaft und Praxis. 70(2019) H.2/3, S.134-140
  2. Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.5, S.462-475
  3. Corbara, S.; Moreo, A.; Sebastiani, F.: Syllabic quantity patterns as rhythmic features for Latin authorship attribution (2023) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.1, S.128-141
  4. Lund, B.D.; Wang, T.; Mannuru, N.R.; Nie, B.; Shimray, S.; Wang, Z.: ChatGPT and a new academic reality : artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing (2023) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.5, S.570-581
  5. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.00 
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    Journal of the Association for Information Science and Technology. 75(2023) no.2, S.167-187
  6. Kajanan, S.; Bao, Y.; Datta, A.; VanderMeer, D.; Dutta, K.: Efficient automatic search query formulation using phrase-level analysis (2014) 0.00 
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    Abstract
    Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1058-1075
  7. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.00 
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    Date
    22. 1.2018 11:32:44
  8. Lee, K.H.; Ng, M.K.M.; Lu, Q.: Text segmentation for Chinese spell checking (1999) 0.00 
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    Source
    Journal of the American Society for Information Science. 50(1999) no.9, S.751-759
  9. Tseng, Y.-H.: Automatic thesaurus generation for Chinese documents (2002) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.13, S.1130-1138
  10. Shaalan, K.; Raza, H.: NERA: Named Entity Recognition for Arabic (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.8, S.1652-1663
  11. Witschel, H.F.: Text, Wörter, Morpheme : Möglichkeiten einer automatischen Terminologie-Extraktion (2004) 0.00 
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    Abstract
    Die vorliegende Arbeit beschäftigt sich mit einem Teilgebiet des TextMining, versucht also Information (in diesem Fall Fachterminologie) aus natürlichsprachlichem Text zu extrahieren. Die der Arbeit zugrundeliegende These besagt, daß in vielen Gebieten des Text Mining die Kombination verschiedener Methoden sinnvoll sein kann, um dem Facettenreichtum natürlicher Sprache gerecht zu werden. Die bei der Terminologie-Extraktion angewandten Methoden sind statistischer und linguistischer (bzw. musterbasierter) Natur. Um sie herzuleiten, wurden einige Eigenschaften von Fachtermini herausgearbeitet, die für deren Extraktion relevant sind. So läßt sich z.B. die Tatsache, daß viele Fachbegriffe Nominalphrasen einer bestimmten Form sind, direkt für eine Suche nach gewissen POS-Mustern ausnützen, die Verteilung von Termen in Fachtexten führte zu einem statistischen Ansatz - der Differenzanalyse. Zusammen mit einigen weiteren wurden diese Ansätze in ein Verfahren integriert, welches in der Lage ist, aus dem Feedback eines Anwenders zu lernen und in mehreren Schritten die Suche nach Terminologie zu verfeinern. Dabei wurden mehrere Parameter des Verfahrens veränderlich belassen, d.h. der Anwender kann sie beliebig anpassen. Bei der Untersuchung der Ergebnisse anhand von zwei Fachtexten aus unterschiedlichen Domänen wurde deutlich, daß sich zwar die verschiedenen Verfahren gut ergänzen, daß aber die optimalen Werte der veränderbaren Parameter, ja selbst die Auswahl der angewendeten Verfahren text- und domänenabhängig sind.
  12. Fegley, B.D.; Torvik, V.I.: On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution (2012) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.11, S.2165-2181
  13. Carrillo-de-Albornoz, J.; Plaza, L.: ¬An emotion-based model of negation, intensifiers, and modality for polarity and intensity classification (2013) 0.00 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1618-1633
  14. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.00 
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    Source
    Information processing and management. 50(2014) no.3, S.443-461
  15. Savoy, J.: Text representation strategies : an example with the State of the union addresses (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.8, S.1858-1870
  16. Collovini de Abreu, S.; Vieira, R.: RelP: Portuguese open relation extraction (2017) 0.00 
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    Abstract
    Natural language texts are valuable data sources in many human activities. NLP techniques are being widely used in order to help find the right information to specific needs. In this paper, we present one such technique: relation extraction from texts. This task aims at identifying and classifying semantic relations that occur between entities in a text. For example, the sentence "Roberto Marinho is the founder of Rede Globo" expresses a relation occurring between "Roberto Marinho" and "Rede Globo." This work presents a system for Portuguese Open Relation Extraction, named RelP, which extracts any relation descriptor that describes an explicit relation between named entities in the organisation domain by applying the Conditional Random Fields. For implementing RelP, we define the representation scheme, features based on previous work, and a reference corpus. RelP achieved state of the art results for open relation extraction; the F-measure rate was around 60% between the named entities person, organisation and place. For better understanding of the output, we present a way for organizing the output from the mining of the extracted relation descriptors. This organization can be useful to classify relation types, to cluster the entities involved in a common relation and to populate datasets.
  17. Kauchak, D.; Leroy, G.; Hogue, A.: Measuring text difficulty using parse-tree frequency (2017) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2088-2100
  18. Çelebi, A.; Özgür, A.: Segmenting hashtags and analyzing their grammatical structure (2018) 0.00 
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    Journal of the Association for Information Science and Technology. 69(2018) no.5, S.675-686
  19. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.00 
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    Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
  20. Geißler, S.: Natürliche Sprachverarbeitung und Künstliche Intelligenz : ein wachsender Markt mit vielen Chancen. Das Beispiel Kairntech (2020) 0.00 
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    Information - Wissenschaft und Praxis. 71(2020) H.2/3, S.95-106

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