莫言小说实体隐喻初探     

王济洲 《德州学院学报》2015,(3):74-78

莫言不仅继承、发展了中国文学的历史传统,而且借鉴了西方作家、学者的创作与研究成果。他在小说中大量使用富有强烈代入感的实体隐喻尤其是身体隐喻,以及雅俗相济的口语化语言,准确而形象地反映了特定时代的社会风貌和个人感受,使其产生了震撼人心的艺术感染力,具有重要的文化意义。  相似文献   

Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system     

Ting Wang  Xinggao Liu  Zeyin Zhang 《Journal of The Franklin Institute》2014

The chaos characteristics of melt index have been first explored, and the Hilbert–Huang transform method and time delay embedding method are applied to multiscale dynamic analysis on the time series of the melt index (MI) in the propylene polymerization industry. The research results show that the embedding delay is 2, the embedding dimension is 5, the correlation dimension D₂ is 1.57, and the maximum Lyapunov exponent is 0.143 for the melt index series, which provide clear evidence of chaotic multiscale features in the propylene polymerization process. Three intrinsic mode functions (IMFs) are decomposed from the melt index time series; the presence of non-integer fractal correlation dimension and positive finite maximum Lyapunov exponent are found in some IMF components. The PP melt index series are divided into two chaotic signals, a determined signal and a random signal respectively, and its complexity is therefore reduced. Furthermore, the coupling of subscale structures of the propylene polymerization is explored with the dimension of interaction dynamics and a robust algorithm for detecting interdependence. It is found that IMF(2) is the main driver in the coupling system of IMF(1)and IMF(2). All these provide a guideline for studying propylene polymerization process with chaotic multiscale theory and may offer more candidate tools to model and control propylene polymerization system in the future.  相似文献   

网络嵌入对技术创新绩效的影响:学习能力的视角          下载免费PDF全文

谢洪明  张颖  程聪  陈盈 《科研管理》2014,35(12):1-8

不同网络嵌入方式对企业创新绩效的影响是存在显著差异的。构建了网络嵌入、学习能力和技术创新绩效之间的理论模型,通过运用结构方程模型对广东省高新技术与民营科技型企业为样本的问卷调查数据进行实证分析。研究结果表明:(1)网络结构嵌入对技术创新绩效没有直接的显著影响,也无法通过学习能力的中介对其产生间接的影响作用;(2)网络关系嵌入对技术创新绩效不仅有直接显著的正向影响,而且还能通过学习能力的部分中介作用对技术创新绩效起到显著的正向影响;(3)在小规模企业中,网络密度对于技术创新绩效的作用并不显著。研究结论进一步深化了技术创新理论,对企业技术创新的提升有一定指导意义。  相似文献   

Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features     

《Information processing & management》2022,59(5):103033

When cybercriminals communicate with their customers in underground markets, they tend to use secure and customizable instant messaging (IM) software, i.e. Telegram. It is a popular IM software with over 700 million monthly active users (MAU) up to June 2022. In recent years, more and more dark jargons (i.e. an innocent-looking replacement of sensitive terms) appear frequently on Telegram. Therefore, jargons identification is one of the most significant research perspectives to track online underground markets and cybercrimes. This paper proposes a novel Chinese Jargons Identification Framework (CJI-Framework) to identify dark jargons. Firstly, we collect chat history from Telegram groups that are related to the underground market and construct the corpus TUMCC (Telegram Underground Market Chinese Corpus), which is the first Chinese corpus in jargons identification research field. Secondly, we extract seven brand-new features which can be classified into three categories: Vectors-based Features (VF), Lexical analysis-based Features (LF), and Dictionary analysis-based Features (DF), to identify Chinese dark jargons from commonly-used words. Based on these features, we then run a statistical outlier detection to decide whether a word is a jargon. Furthermore, we employ a word vector projection method and a transfer learning method to improve the effect of the framework. Experimental results show that CJI-Framework achieves a remarkable performance with an F1-score of 89.66%. After adaptation for English, it performs better than state-of-the-art English jargons identification method as well. Our built corpus and code have been publicly released to facilitate the reproduction and extension of our work.  相似文献   

AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling     

《Information processing & management》2022,59(4):102964

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world. These dialects differ according to the country and region of the speaker, and their textual content is constantly growing with the rise of social media networks and web blogs. Although research on Natural Language Processing (NLP) on standard Arabic, namely Modern Standard Arabic (MSA), has witnessed remarkable progress, research efforts on DA are rather limited. This is due to numerous challenges, such as the scarcity of labeled data as well as the nature and structure of DA. While some recent works have reached decent results on several DA sentence classification tasks, other complex tasks, such as sequence labeling, still suffer from weak performances when it comes to DA varieties with either a limited amount of labeled data or unlabeled data only. Besides, it has been shown that zero-shot transfer learning from models trained on MSA does not perform well on DA. In this paper, we introduce AdaSL, a new unsupervised domain adaptation framework for Arabic multi-dialectal sequence labeling, leveraging unlabeled DA data, labeled MSA data, and existing multilingual and Arabic Pre-trained Language Models (PLMs). The proposed framework relies on four key components: (1) domain adaptive fine-tuning of multilingual/MSA language models on unlabeled DA data, (2) sub-word embedding pooling, (3) iterative self-training on unlabeled DA data, and (4) iterative DA and MSA distribution alignment. We evaluate our framework on multi-dialectal Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks.The overall results show that the zero-shot transfer learning, using our proposed framework, boosts the performance of the multilingual PLMs by 40.87% in macro-F1 score for the NER task, while it boosts the accuracy by 6.95% for the POS tagging task. For the Arabic PLMs, our proposed framework increases performance by 16.18% macro-F1 for the NER task and 2.22% accuracy for the POS tagging task, and thus, achieving new state-of-the-art zero-shot transfer learning performance for Arabic multi-dialectal sequence labeling.  相似文献   

Demand forecasting model using hotel clustering findings for hospitality industry     

《Information processing & management》2022,59(1):102816

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.  相似文献   

基于知识位势的技术创新合作中的知识扩散研究   总被引：7，自引：0，他引：7  

李莉  党兴华  张首魁 《科学学与科学技术管理》2007,28(4):107-112

从知识位势的角度出发,对企业技术创新合作中的知识扩散问题进行了研究。定义了有关知识位势的基本概念,探讨了知识主体间进行知识扩散的条件,给出了知识主体的知识位势扩散函数描述,探讨了知识位势的构成要素及相互作用,对知识主体进行知识扩散的影响因素进行了分析。  相似文献   

互联网嵌入对创业团队资源获取行为的影响研究——创业学习的中介作用   总被引：1，自引：0，他引：1  

刘玉国  王晓丹  尹苗苗  董超 《科学学研究》2016,(6):916-922

随着互联网的快速发展与广泛运用,越来越多的创业者通过互联网展开创业活动。本文借鉴俞函斐提出的互联网嵌入的概念,旨在研究互联网嵌入对创业团队资源获取的影响,并验证创业学习是否在互联网嵌入与创业学习间存在中介作用。互联网嵌入用于考察个体与互联网之间的关系,个体应用互联网的频率越多越是有更多机会接收创业相关情报信息,并通过创业学习进一步促进创业团队对资源的获取,本研究证实了互联网嵌入、创业学习与资源获取三者之间的这种关系。  相似文献   

方志类古籍地名识别及系统构建   总被引：4，自引：0，他引：4  

朱锁玲  包平 《中国图书馆学报》2011,37(3)

以地方志资料汇编<方志物产>(广东分卷)为语料,设计并构建了古籍地名识别系统.采用规则与统计相结合的命名实体识别方法,实现了物产地名的自动识别.分析了命名实体识别技术在中国方志类古籍整理中的应用前景,为方志类古籍进行数字化整理、挖掘物产分布、物产引进和传播等相关研究提供了新的途径.  相似文献   

基于主观逻辑扩展的实体行为动态可信模型     

杨晓晖  赵鹏远  石强  田俊峰 《中国科学院研究生院学报》2011,28(6)

从实体行为可信性的主观角度出发,针对传统主观逻辑理论没有考虑主观评测结果随时间动态变化的问题,提出对主观逻辑理论进行动态化多维扩展的思想,将传统的二维观点空间扩展为多维动态观点空间.在综合考虑实体行为的声誉和风险的基础上构建起实体可信度评价体系,提出一个基于主观逻辑扩展的实体行为动态可信评测模型.实验结果表明,该模型对实体恶意行为的反应更加灵敏,检测更加准确,抑制更加有效.  相似文献