941 |
E Pluribus Unum: Bilingualism and Language Loss in the Second Generation
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942 |
Finding Active Membership Functions for Genetic-Fuzzy Data Mining
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943 |
Translating–transliterating named entities for multilingual information access
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944 |
Linguistic Information Quality in Customers' Forward‐Looking Disclosures and Suppliers' Investment Decisions
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945 |
Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain
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946 |
An Efficient Model for NPD Performance Evaluation Using DEMATEL and Fuzzy ANP—Applied to the TFT-LCD Touch Panel Industry in Taiwan
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947 |
Portfolio optimisation of material purchase considering supply risk – A multi-objective programming model
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948 |
Towards High Performance Text Mining: A TextRank-based Method for Automatic Text Summarization
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949 |
Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement
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950 |
Scientific impact at the topic level: A case study in computational linguistics
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Abstract:
In this article, we propose to apply the topic model and topic‐level eigenfactor (TEF) algorithm to assess the relative importance of academic entities including articles, authors, journals, and conferences. Scientific impact is measured by the biased PageRank score toward topics created by the latent topic model. The TEF metric considers the impact of an academic entity in multiple granular views as well as in a global view. Experiments on a computational linguistics corpus show that the method is a useful and promising measure to assess scientific impact.
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URL: https://doi.org/10.1002/asi.21396
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951 |
Scientific impact at the topic level: A case study in computational linguistics
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952 |
"E Pluribus Unum: Bilingualism and Language Loss in the Second Generation"
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953 |
Does dialect similarity add value to banks? Evidence from China
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