Results 211 to 220 of about 78,543 (258)
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Text Knowledge Mining: An Alternative to Text Data Mining
2008 IEEE International Conference on Data Mining Workshops, 2008In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and ...
Daniel Sánchez 0001 +3 more
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2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2019
This tutorial will introduce attendees to the HathiTrust Research Center and its tools and services for computational text analysis research. HTRC leverages the scope and scale of the HathiTrust Digital Library collection to create opportunities for researchers to perform text data mining on subsets of the corpus.
Eleanor Dickson Koehl, Ryan Dubnicek
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This tutorial will introduce attendees to the HathiTrust Research Center and its tools and services for computational text analysis research. HTRC leverages the scope and scale of the HathiTrust Digital Library collection to create opportunities for researchers to perform text data mining on subsets of the corpus.
Eleanor Dickson Koehl, Ryan Dubnicek
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IEEE Internet Computing, 2006
To perform analysis, decision-making, and knowledge management tasks, information systems use an increasing amount of unstructured information in the form of text. This data influx, in turn, has spawned a need to improve the text-mining technologies required for information retrieval, filtering, and classification.
Juan Jose García Adeva, Rafael A. Calvo
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To perform analysis, decision-making, and knowledge management tasks, information systems use an increasing amount of unstructured information in the form of text. This data influx, in turn, has spawned a need to improve the text-mining technologies required for information retrieval, filtering, and classification.
Juan Jose García Adeva, Rafael A. Calvo
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Proceedings. The Twenty-Second Annual International Computer Software and Applications Conference (Compsac '98) (Cat. No.98CB 36241), 2002
Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available. Extracting meaningful patterns from this data is difficult. Current tools for mining structured data are inappropriate for free text.
Chris Clifton, Rick Steinheiser
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Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available. Extracting meaningful patterns from this data is difficult. Current tools for mining structured data are inappropriate for free text.
Chris Clifton, Rick Steinheiser
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2012
The wealth and diversity of neuroscience research are inherent characteristics of the discipline that can give rise to some complications. As the field continues to expand, we generate a great deal of data about all aspects, and from multiple perspectives, of the brain, its chemistry, biology, and how these affect behavior.
Kyle H, Ambert, Aaron M, Cohen
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The wealth and diversity of neuroscience research are inherent characteristics of the discipline that can give rise to some complications. As the field continues to expand, we generate a great deal of data about all aspects, and from multiple perspectives, of the brain, its chemistry, biology, and how these affect behavior.
Kyle H, Ambert, Aaron M, Cohen
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2022
This chapter presents text mining and its importance, different open-source libraries for natural language processing, data collection, preprocessing concepts, feature selection, and feature extraction techniques, along with visualization techniques.
Mamta Mittal +3 more
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This chapter presents text mining and its importance, different open-source libraries for natural language processing, data collection, preprocessing concepts, feature selection, and feature extraction techniques, along with visualization techniques.
Mamta Mittal +3 more
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Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, 2009
This paper describes techniques for automatically extracting and classifying maps found within articles. The process uses image analysis to find text in maps, document structure to find captions and titles, and then text mining to assign each map to a subject category, a geographical place, and a time period.
Judith Gelernter, Michael E. Lesk
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This paper describes techniques for automatically extracting and classifying maps found within articles. The process uses image analysis to find text in maps, document structure to find captions and titles, and then text mining to assign each map to a subject category, a geographical place, and a time period.
Judith Gelernter, Michael E. Lesk
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Text mining for pharmacogenomics
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics, 2008We are building the Pharmacogenetics & Pharmacogenomics Knowledgebase (PharmGKB, http://www.pharmgkb.org/) with the goal of cataloguing all knowledge about how genetic variation impacts drug response phenotypes. PharmGKB stores primary data (genotype and phenotype data) as well as more distilled knowledge in the form of pathway diagrams, annotated ...
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Semantic pattern mining for text mining
2016 IEEE International Conference on Big Data (Big Data), 2016Pattern mining is a fundamental topic in data mining area. Many pattern mining techniques, such as closed and maximal pattern mining have been proposed for different applications. However, when calculating the frequency of a pattern, the existing techniques treat each word equally.
Xiaoli Song +2 more
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2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2016
Most forms of text contain typos and many algorithms have been proposed to find them, but not much attention has been paid to understanding the occurrence of typos and their indirect significance in understanding how humans interact through text.
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Most forms of text contain typos and many algorithms have been proposed to find them, but not much attention has been paid to understanding the occurrence of typos and their indirect significance in understanding how humans interact through text.
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