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Recent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.

We introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.

Experimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.

This study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).

The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.

The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.

Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.

The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.

This paper aims to examine the effect of differential leadership on organizational corruption by developing a measure of organizational corruption and proposing a moderated mediation model. The model focuses on the mediating role of moral disengagement underpinning the relationship between differential leadership and organizational corruption, and the moderating role of organizational justice in influencing the mediation.

The authors collected 210 responses from online participants with full time work experience and 57 MBA students answered the survey offline.

Results showed that differential leadership was positively associated with organizational corruption. Furthermore, moral disengagement fully mediated the relationship between differential leadership and organizational corruption. Besides, distributive justice negatively but procedural justice positively moderated the indirect effect of moral disengagement.

Testing the moderated mediation model helps to advance the theoretical understanding of the mechanisms that underlie the effect of differential leadership on organizational corrupt behavior.

This empirical study provides preliminary evidence of the mediating role of moral disengagement in the positive relationship between differential leadership and organizational corruption. The mediated moderation also extends the finding by adding organizational justice as the moderator to explain how the effect of differential leadership on organizational corruption. Finally, this study provides initial evidence for organizational corruption measure.

The purpose of this paper is to establish an accurate model to quantify the effect of conductor roughness on insertion loss (IL) and provide improved measurements and suggestions for manufacturing good conductive copper lines of printed circuit board.

To practically investigates the modified model of conductor roughness, three different kinds of alternate oxidation treatments were used to provide transmission lines with different roughness. The IL results were measured by a vector net analyzer for comparisons with the modified model results.

An accurate model, with only a 1.8% deviation on average from the measured values, is established. Compared with other models, the modified model is more reliable in industrial manufacturing.

This paper introduces the influence of tiny roughness structures on IL. Besides, this paper discusses the effect of current distribution on IL.

This paper aims to deal with the grey relational problem of panel data with an attribute value of interval numbers. The grey relational model of interval number for panel data is constructed in this paper.

First, three kinds of interval grey relational operators for the behavior sequence of a dimensionless system are proposed. At the same time, the positive treatment method of interval numbers for cost-type and moderate-type indicators is put forward. On this basis, the correlation between the three-dimensional interval numbers of panel data is converted into the correlation between the two-dimensional interval numbers in time series and cross-sectional dimensions. The grey correlation coefficients of each scheme and the ideal scheme matrix are calculated in the two dimensions, respectively. Finally, the correlation degree of panel interval number and scheme ordering are obtained by arithmetic mean.

This paper proves that the grey relational model of the panel interval number still has the properties of normalization, uniqueness and proximity. It also avoids the problem that the results are not unique due to the different orders of objects in the panel data.

The effectiveness and practicability of the model is verified by taking supplier selection as an example. In fact, this model can also be widely used in agriculture, industry, society and other fields.

The accuracy of the relational results is higher and more accurate compared with the previous studies.

The purpose of this paper is to study the advance selling strategies for oligopolists when considering the product diffusion effect.

The authors consider a market that composes of two competitive sellers who are different in their reputation. The two firms sell the same product in the market over two periods (i.e. the advance selling season and the regular selling season). Due to the effect of product diffusion, the demand of each firm in the regular selling season is dependent on the two firms’ advance demands.

For the firm with lower reputation, it is beneficial to decrease the advance selling price with the diffusion effect caused by its advance demand. For the firm with higher reputation, it is also beneficial to decrease the advance selling price with the diffusion effect caused by its advance demand if the consumers’ enthusiasm for the product in regular selling season is high enough; otherwise it should not decrease his advance selling price since this practice cannot greatly increase his demand.

The obtained results can provide operational managers in reality with valuable suggestions in making advance selling decisions.

The paper is among the first to investigate the impact of product diffusion effect on a firm’s advance selling strategy in a competitive setting.

–The purpose of this paper is to report on an applied research project involving the application of core learning and pedagogical theory to a specific unit in a transnational undergraduate business course.

The project sought to collect data and learning experiences based upon intensive literature reviews and a combined quantitative-qualitative research method. Established research constructs and recent lessons from the literature were applied to the two-year reform cycle of an undergraduate business unit.

The findings validated the research constructs and frameworks employed and reinforced the case for enhancing the nexus between alignment, student motivation, active learning and the international-transnational perspective. Despite initiatives to engender openness and interactivity in the classroom, including dynamic and innovative approaches to communication and content delivery neither the operation of the class nor the eventual performance of the students lived up to expectations.

The research was limited to the experiences of three different cohorts of students on the same unit over a two-year period. Implications are that the same research method and approach are valid for other units either in the same faculty or across faculties.

Serves as an example of what can and cannot be achieved by academics seeking to align their teaching and research activities on a relatively modest basis.

Raises questions as to the social dimension to transnational higher education courses.

While not entirely original, the paper adds value in the form of “lessons learned” from an applied classroom-based research.

The study aims to explore and develop a smart route planning system for the cross-docking delivery operations of a large supermarket chain using an action research (AR) approach and assessing through a design science research (DSR) lens.

This study took a problem-solving AR (PAR) approach toward the delivery operational issue of the case firm. The research process has accorded with the solution incubation and the refinement phases defined by a DSR framework. An intervention-based research framework for DSR is developed to assess the validity of this study as a DSR research and derive mid-range theories.

Dramatic operational and financial improvements were achieved for the case firm. Significant and unintended environmental and social benefits were also found. A design proposition (DP) and several mid-range theories are proposed as an extension of AR research to DSR research.

A problem-solving DSR research can be better assessed by the intervention-based DSR framework developed in this study. DSR studies should be encouraged for both practical and theoretical advancement purposes.

A challenging business problem-solving study can be tackled effectively through an industry/academic collaboration taking a PAR approach to deliver substantial values and organization transformational results.

Drivers and store associates are safer with smart delivery operations in the case firm.

There are still limited PAR design science case studies in the supply chain/logistics research literature. The research experience and findings gained from this study provide more insights toward how this type of research can be conducted and assessed.

Since 1996, hyperlinks have been studied extensively by applying existing bibliometric methods. The Web impact factor (WIF), for example, is the online counterpart of the journal impact factor. This paper reviews how this link‐based metric has been developed, enhanced and applied. Not only has the metric itself undergone improvement but also the relevant data collection techniques have been enhanced. WIFs have also been validated by significant correlations with traditional research measures. Bibliometric techniques have been further applied to the Web and patterns that might have otherwise been ignored have been found from hyperlinks. This paper concludes with some suggestions for future research.

The purpose of this paper is to study the essential characteristics of grey relational degree of proximity, to analyse the abstract meaning of grey relational degree of similarity and fully consider the two different relational degree models.

The paper constructed the grey proximity relational degree by using the weighted mean distance. To analyse the motivation of the development of things, this paper constructed the grey similarity degree by using the concept of induced strength. Finally, the two correlation models are weighted by reliability weighting.

The research finding shows that the distance is the essence of the grey relational degree of proximity, and the induced strength is a good explanation of the similarities in the development of things.

The analyses imply that the total amount of water consumption in China has the greatest correlation with the consumption of agricultural water resources, followed by the consumption of industrial water resources, and the least correlation with the consumption of domestic water resources.

The paper succeeds in realizing the essential characteristics of grey relational degree of proximity and the abstract meaning of grey relational degree of similarity. Besides, the resolution of the correlation degree can be greatly improved by reliability weighting.