Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Valentina Emilia Balas,Neha Sharma,Amlan Chakrabar Conference proceedings 2

Hoover

書目名稱Data Management, Analytics and Innovation影響因子(影響力)




書目名稱Data Management, Analytics and Innovation影響因子(影響力)學科排名




書目名稱Data Management, Analytics and Innovation網(wǎng)絡公開度




書目名稱Data Management, Analytics and Innovation網(wǎng)絡公開度學科排名




書目名稱Data Management, Analytics and Innovation被引頻次




書目名稱Data Management, Analytics and Innovation被引頻次學科排名




書目名稱Data Management, Analytics and Innovation年度引用




書目名稱Data Management, Analytics and Innovation年度引用學科排名




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書目名稱Data Management, Analytics and Innovation讀者反饋學科排名

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使人煩燥

armistice

Conference proceedings 2019of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlightin

搖曳的微光

2194-5357 s and solutions for future development.Serves as a referenceThe volume on .Data Management, Analytics and Innovations. presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and d

思考而得

Zheng Xu,Jinping Li,Jianqin Yin,Yanchun Wuracting explicit temporal expressions and probabilistic in extracting implicit boundaries. We introduce event context as a set of features to learn a CRF model. We achieve F1 score of 0.78 in detecting boundaries. We apply rule-based normalization and ordering of identified temporal expressions.

思考而得

https://doi.org/10.1007/978-981-99-8021-5onverted to a tree for better visualization and easy exploration of data. To signify the effectiveness of the approach, we have carried out the experiment and evaluation on 20 Newsgroup Dataset. Results depict that the presented method is considerably successful in forming topic hierarchy.

Original

Hierarchical Summarization of Text Documents Using Topic Modeling and Formal Concept Analysisonverted to a tree for better visualization and easy exploration of data. To signify the effectiveness of the approach, we have carried out the experiment and evaluation on 20 Newsgroup Dataset. Results depict that the presented method is considerably successful in forming topic hierarchy.

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