Traditional topic models are widely used for semantic discovery from long texts. However, they usually fail to mine high-quality topics from short texts (e.g. tweets) due to the sparsity of features and the lack of word co-occurrence patterns. In this paper, we propose a Variational Auto-Encoder Topic Model (VAETM for short) by combining word vector representation and entity vector representation to address the above limitations. Specifically, we first learn embedding representations of each word and each entity by employing a large-scale external corpora and a large and manually edited knowledge graph, respectively. Then we integrated the embedding representations into the variational auto-encoder framework and propose an unsupervised model named VAETM to infer the latent representation of topic distributions. To further boost VAETM, we propose an improved supervised VAETM (SVAETM for short) by considering label information in training set to supervise the inference of latent representation of topic distributions and the generation of topics. Last, we propose KL-divergence-based inference algorithms to infer approximate posterior distribution for our two models. Extensive experiments on three common short text datasets demonstrate our proposed VAETM and SVAETM outperform various kinds of state-of-the-art models in terms of perplexity, NPMI, and accuracy.

Cross-lingual sentiment classification (CLSC) aims to leverage rich-labeled resources in the source language to improve prediction models of a resource-scarce domain in the target language. Existing feature representation learning-based approaches try to minimize the difference of latent features between different domains by exact alignment, which is achieved by either one-to-one topic alignment or matrix projection. Exact alignment, however, restricts the representation flexibility and further degrades the model performances on CLSC tasks if the distribution difference between two language domains is large. On the other hand, most previous studies proposed document-level models or ignored sentiment polarities of topics that might lead to insufficient learning of latent features. To solve the abovementioned problems, we propose a coarse alignment mechanism to enhance the model's representation by a group-to-group topic alignment into an aspect-level fine-grained model. First, we propose an unsupervised aspect, opinion, and sentiment unification model (AOS), which trimodels aspects, opinions, and sentiments of reviews from different domains and helps capture more accurate latent feature representation by a coarse alignment mechanism. To further boost AOS, we propose ps-AOS, a partial supervised AOS model, in which labeled source language data help minimize the difference of feature representations between two language domains with the help of logistics regression. Finally, an expectation-maximization framework with Gibbs sampling is then proposed to optimize our model. Extensive experiments on various multilingual product review data sets show that ps-AOS significantly outperforms various kinds of state-of-the-art baselines.

The main challenge of cross-domain text classification is to train a classifier in a source domain while applying it to a different target domain. Many transfer learning-based algorithms, for example, dual transfer learning, triplex transfer learning, etc., have been proposed for cross-domain classification, by detecting a shared low-dimensional feature representation for both source and target domains. These methods, however, often assume that the word clusters matrix or the clusters association matrix as knowledge transferring bridges are exactly the same across different domains, which is actually unrealistic in real-world applications and, therefore, could degrade classification performance. In light of this, in this paper, we propose a softly associative transfer learning algorithm for cross-domain text classification. Specifically, we integrate two non-negative matrix tri-factorizations into a joint optimization framework, with approximate constraints on both word clusters matrices and clusters association matrices so as to allow proper diversity in knowledge transfer, and with another approximate constraint on class labels in source domains in order to handle noisy labels. An iterative algorithm is then proposed to solve the above problem, with its convergence verified theoretically and empirically. Extensive experimental results on various text datasets demonstrate the effectiveness of our algorithm, even with the presence of abundant state-of-the-art competitors.

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.

A technical standard is an established norm or requirement in regard to technical systems. It is usually a formal document that establishes uniform engineering or technical criteria, methods, processes and practices. Every year, more than 10K standards are released by standardization organizations, such as International Standardization Organization (ISO), American National Standards Institute (ANSI), and China National Institute of Standardization (CNIS). Thereinto, China publishes more than 4K national and industrial standards per year. Confronted with the large amount of standards, how to manage and make full use of these standards efficiently and effectively has become a major issue to be addressed. In this paper, we introduce a mining and visualizing system for large-scale Chinese technical standards from spatial and temporal perspectives. Specifically, we propose an s-index metric to measure the contribution of a drafting unit. Moreover, we develop a multiple dimensional mining and visualizing analysis system for standardization experts and users to explore regional differences and temporal trends of standards in China. Our system has run on 95K Chinese standards spanning from 2001 to 2016 and provided online service. The extensive experiments and case study demonstrate the effectiveness of our proposed system.

叶声如雨 忽然想到,久不见的人其实也可以不见了的,岁月沧桑,彼此都已面目全非;故乡也是这样,"浮生如梦谁非寄.到处能安即是家"的豁达也好,"埋骨何须桑梓地,人生无处不青山"的豪迈也好,"但使主人能醉客,不知何处是他乡"的自我放逐也好,都比"鸟飞返故乡兮,狐死必首丘"要来得超脱,当然更比汲汲于富贵、念念于"富贵而不归故乡,如衣锦夜行"来得自由而快活.

秋天 那么秋天到来已很久了!八月七日立秋,我今天才知道,太阳到达黄经135°,我仍旧与它距离遥遥,距离遥遥我仍旧只觉得它热.据《月令七十二候集解》载:"秋,揪也,物于此而揪敛也."