Corporate public transparency (CPT) is instrumental for companies to establish communications and trust with the public by disclosing and communicating information concerning corporate environmental and social impacts. However, it is still in dispute whether CPT can help promote corporate financial performance (CFP). This paper studied the moderating role of political embeddedness on the relationship between CPT and CFP. We investigate multiple hypotheses about the moderating roles of the political embeddedness including bureaucratic embeddedness (political connections of a chief executive officer (CEO) who was/is a government official or member of political council) and ownership embeddedness (i.e., state-owned enterprises (SOEs)). With the data of 195 observations from top 200 Chinese enterprises ranked by revenue for the years 2014~2016, the results show the following: (1) the relationship of CPT on CFP is moderated by government official and SOE ownership; (2) a negative moderating effect of government official; and (3) a negative moderating effect of SOE ownership. The research implications are further discussed. The findings of this study have practical implications for investors, stakeholders, and regulators.
Relation extraction is a fundamental task in information extraction, which is to identify the semantic relationships between two entities in the text. In this paper, deep belief nets (DBN), which is a classifier of a combination of several unsupervised learning networks, named RBM (restricted Boltzmann machine) and a supervised learning network named BP (back-propagation), is presented to detect and classify the relationships among Chinese name entities. The RBM layers maintain as much information as possible when feature vectors are transferred to next layer. The BP layer is trained to classify the features generated by the last RBM layer. The experiments are conducted on the Automatic Content Extraction 2004 dataset. This paper proves that a character-based feature is more suitable for Chinese relation extraction than a word-based feature. In addition, the paper also performs a set of experiments to assess the Chinese relation extraction on different assumptions of an entity categorization feature. These experiments showed the comparison among models with correct entity types and imperfect entity type classified by DBN and without entity type. The results show that DBN is a successful approach in the high-dimensional-feature-space information extraction task. It outperforms state-of-the-art learning models such as SVM and back-propagation networks.
This paper adapts deep belief networks (DBN) to detect entity mentions in Chinese documents. Our results exhibit how the depth of architecture and quantity of unit in hidden layer influence the performance. Different feature combinations are used to show their advantages and disadvantages in DBN for this task. Moreover, we combined Chinese word segmentation systems to alleviate word segmentation error. Token labels are produced independently by DBN which does not concerned what are the token labels before current word. Viterbi algorithm is a good solution to find the most likely probability label path to make DBN be more effective for entity detection. Furthermore, this paper demonstrates DBN is a proper model for our tasks and its results are better than Support Vector Machine (SVM), Artificial Neural Network (ANN) and Conditional Random Field (CRF).
In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results.
ARTICLE HISTORY
With more and more text‐image co‐occurrence data becoming available on the Web, we are interested in how text especially Chinese context around images can aid image classification. The goal is to construct a classification system for images, and we used the context of the images to improve the classification system. First, we extracted three kinds of features, including global visual features, local visual features, and text features using both the image content and context. Then, we tried various feature combination methods and train classifiers for each kind of feature vector. Finally, we used a classifier fusion strategy based on weight learning, combining classifier outputs together, and we obtained the category of unlabeled images. In our experiments on the data set extracted from Google Image Search, we demonstrated the benefit of using context to help image classification. By comparing different feature combination methods on our feature set, we adopted the most effective one. Meanwhile, the classifier fusion approach improves the classification accuracy.
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