7,457 research outputs found
Chinese Semantic Class Learning from Web Based on Concept-Level Characteristics
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Expanding Person-to-Person Diplomacy with Overseas Chinese
In the current global context, with prominent multilateralism and multipolarity trends, China is seeing a rise in comprehensive strength and influence on the international stage. China has always upheld mutual respect, mutual benefits, and win-win situations for its diplomatic strategies and has launched the Belt and Road Initiative to actively unite neighboring countries and regions for common development and advancement. Person-to-person diplomacy is an important part of foreign affairs work, of which overseas Chinese are a force to be reckoned with. Overseas Chinese are also descendants of the Chinese nation, so it is easier to communicate with them concerning culture, language, characters, and ideas. Therefore, with Chinese in Indonesia as the research subjects, this paper provides a case study of the Confucius Institute jointly built by Xihua University and Universitas Sebelas Maret (Sebelas Maret University, UNS) to specifically explore how to expand person-to-person diplomacy, unite overseas Chinese, promote China-foreign exchanges and cooperation, and enhance mutual understanding and friendship between the two peoples with overseas Chinese as the bridge.
Keywords: people-to-people diplomacy, confucius institute, overseas Chines
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Multi-label learning deals with the classification problems where each
instance can be assigned with multiple labels simultaneously. Conventional
multi-label learning approaches mainly focus on exploiting label correlations.
It is usually assumed, explicitly or implicitly, that the label sets for
training instances are fully labeled without any missing labels. However, in
many real-world multi-label datasets, the label assignments for training
instances can be incomplete. Some ground-truth labels can be missed by the
labeler from the label set. This problem is especially typical when the number
instances is very large, and the labeling cost is very high, which makes it
almost impossible to get a fully labeled training set. In this paper, we study
the problem of large-scale multi-label learning with incomplete label
assignments. We propose an approach, called MPU, based upon positive and
unlabeled stochastic gradient descent and stacked models. Unlike prior works,
our method can effectively and efficiently consider missing labels and label
correlations simultaneously, and is very scalable, that has linear time
complexities over the size of the data. Extensive experiments on two real-world
multi-label datasets show that our MPU model consistently outperform other
commonly-used baselines
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