-
Mining text data (2012)
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- Abstract
- Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
- LCSH
- Computer science
Computer Communication Networks
- RSWK
- Text Mining / Aufsatzsammlung
- Subject
- Text Mining / Aufsatzsammlung
Computer science
Computer Communication Networks
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Tonkin, E.L.; Tourte, G.J.L.: Working with text. tools, techniques and approaches for text mining (2016)
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- RSWK
- Text Mining / Aufsatzsammlung
- Subject
- Text Mining / Aufsatzsammlung
-
Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016)
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- Abstract
- We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
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- Date
- 22. 3.2013 19:43:01
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Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
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Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
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- Date
- 7. 3.2019 16:32:22
-
Jäger, L.: Von Big Data zu Big Brother (2018)
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- Date
- 22. 1.2018 11:33:49
