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A Comparison of Different Strategies for Automated Semantic Document Annotation
We introduce a framework for automated semantic document annotation that is composed of four processes, namely concept extraction, concept activation, annotation selection, and evaluation. The framework is used to implement and compare different annotation strategies motivated by the literature. For concept extraction, we apply entity detection with semantic hierarchical knowledge bases, Tri-gram, RAKE, and LDA. For concept activation, we compare a set of statistical, hierarchy-based, and graph-based methods. For selecting annotations, we compare top-k as well as kNN. In total, we define 43 different strategies including novel combinations like using graph-based activation with kNN. We have evaluated the strategies using three different datasets of varying size from three scientific disciplines (economics, politics, and computer science) that contain 100, 000 manually labeled documents in total. We obtain the best results on all three datasets by our novel combination of entity detection with graph-based activation (e.g., HITS and Degree) and kNN. For the economic and political science datasets, the best F-measure is .39 and .28, respectively. For the computer science dataset, the maximum F-measure of .33 can be reached. The experiments are the by far largest on scholarly content annotation, which typically are up to a few hundred documents per dataset only.
Gregor Große-Bölting, Chifumi Nishioka, and Ansgar Scherp. 2015. A Comparison of Different Strategies for Automated Semantic Document Annotation. In Proceedings of the 8th International Conference on Knowledge Capture (K-CAP 2015). ACM, New York, NY, USA, , Article 8 , 8 pages. DOI=http://dx.doi.org/10.1145/2815833.2815838
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The king had a strange custom of eating a covered dish alone each day. His servant tasted the dish one day and gained the ability to understand animals. He overheard ducks and learned a duck swallowed the queen's ring. This proved the servant's innocence when the ring was found. As thanks, he requested a horse and money. He helped various animals on his journey who later rewarded him by helping find the golden apple, completing tasks to win the princess' hand in marriage.
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A Comparison of Different Strategies for Automated Semantic Document Annotation
We introduce a framework for automated semantic document annotation that is composed of four processes, namely concept extraction, concept activation, annotation selection, and evaluation. The framework is used to implement and compare different annotation strategies motivated by the literature. For concept extraction, we apply entity detection with semantic hierarchical knowledge bases, Tri-gram, RAKE, and LDA. For concept activation, we compare a set of statistical, hierarchy-based, and graph-based methods. For selecting annotations, we compare top-k as well as kNN. In total, we define 43 different strategies including novel combinations like using graph-based activation with kNN. We have evaluated the strategies using three different datasets of varying size from three scientific disciplines (economics, politics, and computer science) that contain 100, 000 manually labeled documents in total. We obtain the best results on all three datasets by our novel combination of entity detection with graph-based activation (e.g., HITS and Degree) and kNN. For the economic and political science datasets, the best F-measure is .39 and .28, respectively. For the computer science dataset, the maximum F-measure of .33 can be reached. The experiments are the by far largest on scholarly content annotation, which typically are up to a few hundred documents per dataset only.
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White Snake Text
The king had a strange custom of eating a covered dish alone each day. His servant tasted the dish one day and gained the ability to understand animals. He overheard ducks and learned a duck swallowed the queen's ring. This proved the servant's innocence when the ring was found. As thanks, he requested a horse and money. He helped various animals on his journey who later rewarded him by helping find the golden apple, completing tasks to win the princess' hand in marriage.
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The document analyzes representations of men and women in soap opera trailers. For men, it finds they are portrayed as emotional, aggressive, and having both light and dark sides. Younger men are shown as dangerous through dark costumes. For women, it finds they break stereotypes by showing personalities and strength, though some still portray traditional gender roles. Tracey is an unconventional female antagonist. Teenagers are depicted as naïve, immature, and making poor choices through issues like drugs and pregnancy. Overall, the document examines how characters are represented through techniques like mise-en-scene, camerawork, editing, sound, and dialogue.
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Data mining , knowledge discovery is the process
of analyzing data from different perspectives and summarizing it
into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a
number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize
it, and summarize the relationships identified. Technically, data
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clustering? It can be shown that there is no absolute “best”
criterion which would be independent of the final aim of the
clustering. Consequently, it is the user which must supply this
criterion, in such a way that the result of the clustering will suit
their needs.
For instance, we could be interested in finding
representatives for homogeneous groups (data reduction), in
finding “natural clusters” and describe their unknown properties
(“natural” data types), in finding useful and suitable groupings
(“useful” data classes) or in finding unusual data objects (outlier
detection).Of late, clustering techniques have been applied in the
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la procaine 8ÈME JOURNÉE ROUENNAISE DE FORMATION ET D'INFORMATION de l'ARLIN consacrée à l'HYGIENE ET PRÉVENTION DU RISQUE INFECTIEUX EN ÉTABLISSEMENTS MÉDICO-SOCIAUX se déroulera le JEUDI 1ER DÉCEMBRE 2016
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• Présentation de NormAntibio
• Résultats de l’enquête sur la gestion des excrétas en EHPAD
• Usage raisonné des détergents désinfectants pour l’entretien des locaux en EMS (hors épidémie)
• Vaccination des professionnels de santé
• Modéliser la diffusion de la grippe saisonnière dans l'unité gériatrique d'un hôpital"
• Etude VaxEHPAD
• Antisepsie de la peau saine avant un acte invasif chez l’adulte
• Un point sur les déchets
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Data mining , knowledge discovery is the process
of analyzing data from different perspectives and summarizing it
into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a
number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize
it, and summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns among
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clustering is to determine the intrinsic grouping in a set of
unlabeled data. But how to decide what constitutes a good
clustering? It can be shown that there is no absolute “best”
criterion which would be independent of the final aim of the
clustering. Consequently, it is the user which must supply this
criterion, in such a way that the result of the clustering will suit
their needs.
For instance, we could be interested in finding
representatives for homogeneous groups (data reduction), in
finding “natural clusters” and describe their unknown properties
(“natural” data types), in finding useful and suitable groupings
(“useful” data classes) or in finding unusual data objects (outlier
detection).Of late, clustering techniques have been applied in the
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