We propose an unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a two-stage stochastic process. An author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words. The probability distribution over topics in a multi-author paper is a mixture of the distributions associated with the authors. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to three large text corpora: 150,000 abstracts from the CiteSeer digital library, 1740 papers from the Neural Information Processing Systems (NIPS) Conferences, and 121,000 emails from the Enron corporation. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, parsing of abstracts by topics and authors, and detection of unusual papers by specific authors. Experiments based on perplexity scores for test documents and precision-recall for document retrieval are used to illustrate systematic differences between the proposed author-topic model and a number of alternatives. Extensions to the model, allowing for example, generalizations of the notion of an author, are also briefly discussed.

知识图谱作为快速兴起和发展的跨学科研究领域,在国内外不同学科间的发展轨迹、研究重点等方面总体上存在差异。适时梳理国内外该领域发展历史上的重要理论和技术发展轨迹,明晰核心人物和团队,可为当前和今后基于知识图谱的可视化研究提供理论基础。本文选取1998—2014年间SSCI、CSSCI中以知识图谱为主题的期刊论文,以SATI、UCINET、Net Draw、Cite Space等为数据分析和可视化工具,通过时间分布揭示该领域发展的阶段特征,通过节点性论文计算和高频关键词共现分析揭示该领域发展的内容分布,从而厘清其发展脉络;从学科分布、核心期刊和边缘期刊的判别揭示该领域发展的跨学科概貌,通过核心作者综合指数计算、合作分析和机构分析揭示该领域研究的人物关系,厘清其发展流派。在此基础上提出知识图谱研究的弱化与主题的衍生、知识图谱的跨学科研究与应用和知识创造者的合作创新三个发展趋势。图11。表3。参考文献29。

The paper introduces a new effective model for topic recognition. The model follows a multi-expert decision paradigm based on fuzzy relations in which fuzzy variables express degrees of reliability of expert decision. Heterogeneous measures are integrated by the fuzzy relations whose structure and components may evolve in time. Experiments resulted in more than 80% topic classification accuracy on articles of the French newspaper Le Monde which describe a very large variety of facts with a very large vocabulary (of the order of 500 000 words). Experiments show a significant improvement when the above mentioned integration of multi-expert decision is used. A robust strategy for dynamic language model (LM) selection, based on topic recognition and switching between topic models, is proposed. It is effective because it relies on a small set of well trained topic-dependent LMs and on reliable topic recognition. By using perplexity as a performance measure of the LM switching model, a tangible reduction is observed with respect to the use of a single, general, static LM.

LPSS是由多种功能模块组成的智能学习支持系统,概率潜在语义分析PLSA是基于概率的一种新的潜在语义分析算法.文中对比论述了PLSA与LSA算法,并在此基础上分析了其他构建学习支持系统的方法,阐明PLSA相比于其他主主法的优势.提出了基于PLSA的学习支持系统的具体设计与实现.

以OHSUMED语料库内提供的明确相关提问对为金标准和研究材料，借助BICOMB软件生成主题词一来源文献矩阵和共词矩阵，并获得各种系数的相似（相异）矩阵，对比分析目前国内基于SPSS共现聚类分析过程中主题词一来源文献矩阵与共现矩阵、各种相似性参数和各种类间距离计算方法的聚类效果。结果表明：主题词一来源文献矩阵聚类结果优于共词矩阵，在聚类分析中应优先选择。共词矩阵选择相似系数时应结合实际矩阵数据性质，并注意聚类方法原理上的正确性。

Chinese spelling check (CSC) is still an unsolved problem today since there are many homonymous or homomorphous characters. Recently, more and more CSC systems have been proposed. To the best of our knowledge, language modeling is one of the major components among these systems because of its simplicity and moderately good predictive power. After deeply analyzing the school of research, we are aware that most of the systems only employ the conventional n-gram language models. The contributions of this article are threefold. First, we propose a novel probabilistic framework for CSC, which naturally combines several important components, such as the substitution model and the language model, to inherit their individual merits as well as to overcome their limitations. Second, we incorporate the topic language models into the CSC system in an unsupervised fashion. The topic language models can capture the long-span semantic information from a word (character) string while the conventional n-gram language models can only preserve the local regularity information. Third, we further integrate Web resources with the proposed framework to enhance the overall performance. Our rigorously empirical experiments demonstrate the consistent and utility performance of the proposed framework in the CSC task.

随着移动应用的急速增长,手机助手等移动应用获取平台也面临着信息过载的问题.面对大量的移动应用,用户很难找到最适合的;而另一方面,长尾应用淹没在资源池中不易被人所知.已有推荐方法多注重推荐准确率,忽视了多样性,推荐结果中多是下载量高的应用,使得推荐系统的数据积累越来越偏向于热门应用,导致长期的推荐效果越来越差.针对这一问题,首先改进了两种推荐方法,提出了将用户的主题模型和应用的主题模型与MF相结合的LDA_MF模型,以及将应用的标签信息和用户行为数据同时加以考虑的LDA_CF算法.为了结合不同算法的优点,在保证推荐准确率的条件下提升推荐结果的多样性,提出了融合LDA_MF,LDA_CF以及经典的基于物品的协同过滤模型的混合推荐算法.使用真实的大数据评测所提推荐算法,结果显示,所提推荐方法能够得到推荐多样性更好且准确率更高的结果.

In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes. To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention. This paper proposes a topic tracking language model that can adaptively track changes in topics based on current text information and previously estimated topic models in an on-line manner. The proposed model is applied to language model adaptation in speech recognition. We use the MIT OpenCourseWare corpus and Corpus of Spontaneous Japanese in speech recognition experiments, and show the effectiveness of the proposed method.

基于概率主题模型的图像标注方法旨在通过学习图像语义进行图像标注，近年来倍受研究人员关注。考虑到类别对图像标注可提供有价值的信息，例如，“高楼”类图像，出现“天空”、“摩天楼”的可能性大于“海水”和“沙滩”。而“海岸”类图像出现“海水”、“沙滩”的可能性要大于“天空”和“摩天楼”。在Corr-LDA模型的基础上利用图像类别来改进图像的标注性能，提出了一个融入类别信息的图像标注概率主题模型。为该模型推导了一个基于变分EM的参数估计算法，并给出了使用该模型标注图像的方法。在LabelMe和UIUC-Sport两个真实数据集上验证了提出模型的标注性能要高于其他相比较模型。

It has recently been suggested that assuming independence between labels is not suitable for real-world multi-label classification. To account for label dependencies, this paper proposes a supervised topic modeling algorithm, namely labelset topic model (LsTM). Our algorithm uses two labelset layers to capture label dependencies. LsTM offers two major advantages over existing supervised topic modeling algorithms: it is straightforward to interpret and it allows words to be assigned to combinations of labels, rather than a single label. We have performed extensive experiments on several well-known multi-label datasets. Experimental results indicate that the proposed model achieves performance on par with and often exceeding that of state-of-the-art methods both qualitatively and quantitatively.

Recent findings show that online reviews, blogs, and discussion forums on chronic diseases and drugs are becoming important supporting resources for patients. Extracting information from these substantial bodies of texts is useful and challenging. We developed a generative probabilistic aspect mining model (PAMM) for identifying the aspects/topics relating to class labels or categorical meta-information of a corpus. Unlike many other unsupervised approaches or supervised approaches, PAMM has a unique feature in that it focuses on finding aspects relating to one class only rather than finding aspects for all classes simultaneously in each execution. This reduces the chance of having aspects formed from mixing concepts of different classes; hence the identified aspects are easier to be interpreted by people. The aspects found also have the property that they are class distinguishing: They can be used to distinguish a class from other classes. An efficient EM-algorithm is developed for parameter estimation. Experimental results on reviews of four different drugs show that PAMM is able to find better aspects than other common approaches, when measured with mean pointwise mutual information and classification accuracy. In addition, the derived aspects were also assessed by humans based on different specified perspectives, and PAMM was found to be rated highest.

为了从日益丰富的蒙古文信息中快速准确地检索用户需求的主题信息，提出了一种融合主题模型LDA与语言模型的方法。该方法用语言模型与LDA模型对蒙古文文档进行建模，利用吉普斯抽样方法进行推理间接计算模型的参数，挖掘隐藏在文档内不同主题与词之间的关系，得到文档的主题分布，并以此分布来计算与检索关键词主题之间的相似度，最后返回与该主题最相关的文档。语言模型充分利用蒙古文语法特征以及统计信息，而主题模型LDA又具有良好的潜在语义挖掘及主题发现的泛化学习能力，从而结合两种方法更好地实现蒙古文文档的主题语义检索，提高检索准确性。实验结果表明，融合LDA与语言模型的方法相比单一模型体现主题语义方面取得了较好的效果。

The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA),which has been one of the most popular probabilistic topic modeling methods in the past decade. However, batch EM has high time and space complexities to learn big LDA models from big data streams. In this paper, we present a fast online EM (FOEM) algorithm that infers the topic distribution from the previously unseen documents incrementally with constant memory requirements. Within the stochastic approximation framework, we show that FOEM can converge to the local stationary point of the LDA likelihood function. By dynamic scheduling for the fast speed and parameter streaming for the low memory usage, FOEM is more efficient for some lifelong topic modeling tasks than the state-of-the-art online LDA algorithms to handle both big data and big models (aka, big topic modeling) on just a PC.

<p>文章从新浪微博和Twitter抓取突发事件语料，根据主题模型确定候选话题，通过对候选话题进行聚类确定更为合适的话题数，然后根据主题模型结果计算相邻时间片话题之间的相似度，在此基础上分析话题的演化，最终完成中英文话题演化的比较分析。文章针对H7N9微博的实证结果表明：新浪微博话题数目较多，话题面更为广泛；国内对H7N9禽流感事件的爆发反应更为强烈；两个平台在话题内容方面也存在一些差异；另外，两个平台话题演化的可视化结果可以描述H7N9禽流感事件新话题的产生、旧话题的消亡以及话题内容随时间的变化。</p>

为了更准确更合理地进行科研合作推荐,文章运用社会网络理论,在社区划分的基础上,构建基于LDA的作者兴趣模型,然后对作者的相关文献进行分析,实现科研合作推荐的目的。实验证明,该方法能够有效地进行科研合作的推荐。

随着微博的日趋流行,Twitter等微博网站已成为海量信息的发布体,对微博的研究也需要从单一的用户关系分析向微博本身内容的挖掘进行转变.在数据挖掘领域,尽管传统文本的主题挖掘已经得到了广泛的研究,但对于微博这种特殊的文本,因其本身带有一些结构化的社会网络方面的信息,传统的文本挖掘算法不能很好地对它进行建模.提出了一个基于LDA的微博生成模型MB-LDA,综合考虑了微博的联系人关联关系和文本关联关系,来辅助进行微博的主题挖掘.采用吉布斯抽样法对模型进行推导,不仅能挖掘出微博的主题,还能挖掘出联系人关注的主题.此外,模型还能推广到许多带有社交网络性质的文本中.在真实数据集上的实验表明,MB-LDA模型能有效地对微博进行主题挖掘.

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word \(\times \) time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where the mixing weights are defined by the motif starting time occurrences. The novelties are multi fold. First, unlike previous approaches where topics modeled only the co-occurrence of words at a given time instant, our motifs model the co-occurrence and temporal order in which the words occur within a temporal window. Second, unlike traditional Dynamic Bayesian networks (DBN), our model accounts for the important case where activities occur concurrently in the video (but not necessarily in synchrony), i.e., the advent of activity motifs can overlap. The learning of the motifs in these difficult situations is made possible thanks to the introduction of latent variables representing the activity starting times, enabling us to implicitly align the occurrences of the same pattern during the joint inference of the motifs and their starting times. As a third novelty, we propose a general method that favors the recovery of sparse distributions, a highly desirable property in many topic model applications, by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria. We substantiate our claims with experiments on synthetic data to demonstrate the algorithm behavior, and on four video datasets with significant variations in their activity content obtained from static cameras. We observe that using low-level motion features from videos, our algorithm is able to capture sequential patterns that implicitly represent typical trajectories of scene objects.

自动挖掘科技文献主题并识别主题变化对于科研工作者及时获取相关领域的最新研究动态有着重要作用。针对科技文献主题多样、动态性强等特点，分析科技文献主题发现及演化具体方法，基于层次概率主题模型 hLDA，采用 Gibbs抽样来进行模型参数估计，并运用互信息的方法对主题词进行筛选，以提取高质量的主题词。最后，利用先／后离散分析方法研究主题随时间的演化问题。实验结果验证了主题发现及演化方法的可行性及有效性。

为了解决稀疏情况下的自动文档问题，将提出的完全稀疏主题模型引入到文档中。根据模型中主题分布和主题的词汇分布，提出了该模型上的自动算法。为了验证该方法的有效性，在DUC2007数据集上使用ROUGE自动评测工具进行评测。通过与DUC2007中专家和对多种实验的比较，比较结果表明，该方法在准确率上显著优于其它方法，同时在推断时间、方法的简单性等各个方面也都具有优势。