PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks

Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular machine learning tasks. One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task. Although the low dimensional representations learned are applicable to many different tasks, they are not particularly tuned for any task. In this paper, we fill this gap by proposing a semi-supervised representation learning method for text data, which we call the \textit{predictive text embedding} (PTE). Predictive text embedding utilizes both labeled and unlabeled data to learn the embedding of text. The labeled information and different levels of word co-occurrence information are first represented as a large-scale heterogeneous text network, which is then embedded into a low dimensional space through a principled and efficient algorithm. This low dimensional embedding not only preserves the semantic closeness of words and documents, but also has a strong predictive power for the particular task. Compared to recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more effective, much more efficient, and has fewer parameters to tune.Comment: KDD 201

A Study on the Countermeasures of Agricultural Mechanization Development

This paper analyzes the current situation of agricultural mechanization development in the process of rural revitalization

State-owned Enterprises Investment Management Status and Optimization Strategy

In recent years in China’s booming market economy, along with the transformation of the market economy on the reform of the state-owned economic system also came into being, in order to increase the source of profit channels of state-owned enterprises, do a good job of the value-added work of state-owned enterprise assets, many state-owned enterprises choose to diversify development, and this trend is now very obvious. The development of enterprises has also produced a number of problems that have had a significant impact on the quality and returns of investments, causing unnecessary losses and increasing unnecessary risks for many state-owned enterprises. The article will be based on the current situation of investment projects and management of state-owned enterprises, analyze the problems that exist in them, put forward effective improvement measures and targeted countermeasure suggestions for the investment management of state-owned enterprises, and promote the high-quality development of state-owned enterprises

LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.Comment: WWW 201

Low-lying states in 30^{30}Mg: a beyond relativistic mean-field investigation

The recently developed model of three-dimensional angular momentum projection plus generator coordinate method on top of triaxial relativistic mean-field states has been applied to study the low-lying states of $^{30}$Mg. The effects of triaxiality on the low-energy spectra and E0 and E2 transitions are examined.Comment: 6 pages, 3 figures, 1 table, talk presented at the 17th nuclear physics conference "Marie and Pierre Curie" Kazimierz Dolny, 22-26th September 2010, Polan

Rapid structural change in low-lying states of neutron-rich Sr and Zr isotopes

The rapid structural change in low-lying collective excitation states of neutron-rich Sr and Zr isotopes is tudied by solving a five-dimensional collective Hamiltonian with parameters determined by both relativistic mean-field and non-relativistic Skyrme-Hartree-Fock calculations using the PC-PK1 and SLy4 forces respectively. Pair correlations are treated in BCS method with either a separable pairing force or a density-dependent zero-range force. The isotope shifts, excitation energies, electric monopole and quadrupole transition strengths are calculated and compared with corresponding experimental data. The calculated results with both the PC-PK1 and SLy4 forces exhibit a picture of spherical-oblate-prolate shape transition in neutron-rich Sr and Zr isotopes. Compared with the experimental data, the PC-PK1 (or SLy4) force predicts a more moderate (or dramatic) change in most of the collective properties around N=60. The underlying microscopic mechanism responsible for the rapid transition is discussed.Comment: 10 pages (twocolumn), 10 figure