L-fuzzy approximating spaces

Zeng, Jiasheng; Wang, Pei

doi:10.3233/ifs-182766

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…In a statistical analysis of two-character words in the Grading Syllabus for Chinese Vocabulary and Chinese Characters (GSCVCC; Chinese Proficiency Test Center, 2001), Xing (2006) found that only 37% of the characters had fixed positions in forming words, and 63% were not positionally constrained. To the best of our knowledge, Zeng's (2008) database is the only available resource that provides a list of productive word formation morphemes/affixoids in Chinese, accompanied by morpheme frequency. His database is critical because it provides an explicit description of four inclusion criteria: (a) productivity, (b) position stability, (c) desemantization (with weakened lexical meaning), and (d) boundness (i.e., the morpheme cannot be used as an independent lexical unit).…”

Section: Morphological Properties In Chinesementioning

confidence: 99%

Contributions of Morphological Awareness to Adult L2 Chinese Word Meaning Inferencing

Koda

2017

The Modern Language Journal

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This study examined the contributions of morphological awareness (MA) to second language (L2) word meaning inferencing in English‐speaking adult learners of Chinese (N = 50). Three research questions were posed: Are L2 learners sensitive to the morphological structure of unknown multi‐character words? Does first language (L1) MA contribute to L2 MA over and above L2 linguistic knowledge? Does L2 MA contribute to L2 word meaning inference over and above L1 MA and L2 linguistic knowledge? These questions were investigated through the use of a set of experimental and paper‐and‐pencil measurements of the aforementioned L1 and L2 reading subskills and L2 linguistic knowledge, as well as working memory. Several significant results were found. The L2 learners were sensitive to the morphological structure of multi‐character words and more successful in guessing the meanings of novel words containing affixoids and familiar bases. L1 MA was found to transfer and facilitate the development of L2 MA over and above L2 linguistic knowledge. Additionally, L2 MA contributed to L2 word meaning inferencing indirectly through L2 linguistic knowledge. The discussion focuses on the intralingual and interlingual relationships among L1 MA, L2 MA, L2 linguistic knowledge, and L2 word meaning inferencing in adult L2 reading development.

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Section: Morphological Properties In Chinesementioning

confidence: 99%

Contributions of Morphological Awareness to Adult L2 Chinese Word Meaning Inferencing

Koda

2017

The Modern Language Journal

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Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

Xiao

Sun

Han³

et al. 2021

Appl Intell

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The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods.

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A Dynamic Emergency Decision-Making Method Based on Group Decision Making with Uncertainty Information

Zheng

Wang

Zhao

et al. 2020

Int J Disaster Risk Sci

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In emergency decision making (EDM), it is necessary to generate an effective alternative quickly. Case-based reasoning (CBR) has been applied to EDM; however, choosing the most suitable case from a set of similar cases after case retrieval remains challenging. This study proposes a dynamic method based on case retrieval and group decision making (GDM), called dynamic case-based reasoning group decision making (CBRGDM), for emergency alternative generation. In the proposed method, first, similar historical cases are identified through case similarity measurement. Then, evaluation information provided by group decision makers for similar cases is aggregated based on regret theory, and comprehensive perceived utilities for the similar cases are obtained. Finally, the most suitable historical case is obtained from the case similarities and the comprehensive perceived utilities for similar historical cases. The method is then applied to an example of a gas explosion in a coal company in China. The results show that the proposed method is feasible and effective in EDM. The advantages of the proposed method are verified based on comparisons with existing methods. In particular, dynamic CBRGDM can adjust the emergency alternative according to changing emergencies. The results of application of dynamic CBRGDM to a gas explosion and comparison with existing methods verify its feasibility and practicability.

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L-fuzzy approximating spaces

Cited by 4 publications

References 46 publications

Contributions of Morphological Awareness to Adult L2 Chinese Word Meaning Inferencing

Contributions of Morphological Awareness to Adult L2 Chinese Word Meaning Inferencing

Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

A Dynamic Emergency Decision-Making Method Based on Group Decision Making with Uncertainty Information

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