Topic models based on latent Dirichlet al-location and related methods are used in a range of user-focused tasks including doc-ument navigation and trend analysis, but evaluation of the intrinsic quality of the topic model and topics remains an open research area. In this work, we explore the two tasks of automatic evaluation of single topics and automatic evaluation of whole topic models, and provide recom-mendations on the best strategy for per-forming the two tasks, in addition to pro-viding an open-source toolkit for topic and topic model evaluation. 1

Quantitative notions of diversity have been explored across a variety of disciplines ranging from conservation biology to economics. However, there has been relatively little work on measuring the diversity of text documents via their content. In this paper we present a text-based framework for quantifying how diverse a document is in terms of its content. The proposed approach learns a topic model over a corpus of documents, and computes a distance matrix between pairs of topics using measures such as topic co-occurrence. These pairwise distance measures are then combined with the distribution of topics within a document to estimate each document's diversity relative to the rest of the corpus. The method provides several advantages over existing methods. It is fully data-driven, requiring only the text from a corpus of documents as input, it produces human-readable explanations, and it can be generalized to score diversity of other entities such as authors, academic departments, or journals. We describe experimental results on several large data sets which suggest that the approach is effective and accurate in quantifying how diverse a document is relative to other documents in a corpus.

We present TopicNets , a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to support knowledge discovery are presented. These include corpus- and document-specific views, iterative topic modeling, search, and visual filtering. Drill-down functionality is provided to allow analysts to visualize individual document sections and their relations within the global topic space. Analysts can search across a dataset through a set of expansion techniques on selected document and topic nodes. Furthermore, analysts can select relevant subsets of documents and perform real-time topic modeling on these subsets to interactively visualize topics at various levels of granularity, allowing for a better understanding of the documents. A discussion of the design and implementation choices for each visual analysis technique is presented. This is followed by a discussion of three diverse use cases in which TopicNets enables fast discovery of information that is otherwise hard to find. These include a corpus of 50,000 successful NSF grant proposals, 10,000 publications from a large research center, and single documents including a grant proposal and a PhD thesis.

Latent Dirichlet allocation (LDA) is a popular algorithm for discovering semantic structure in large collections of text or other data. Although its complexity is linear in the data size, its use on increasingly massive collections has created considerable interest in parallel implementations. “Approximate distributed” LDA, or AD-LDA, approximates the popular collapsed Gibbs sampling algorithm for LDA models while running on a distributed architecture. Although this algorithm often appears to perform well in practice, its quality is not well understood theoretically or easily assessed on new data. In this work, we theoretically justify the approximation, and modify AD-LDA to track an error bound on performance. Specifically, we upper bound the probability of making a sampling error at each step of the algorithm (compared to an exact, sequential Gibbs sampler), given the samples drawn thus far. We show empirically that our bound is sufficiently tight to give a meaningful and intuitive measure of approximation error in AD-LDA, allowing the user to track the tradeoff between accuracy and efficiency while executing in parallel.

In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on real world text corpora. Conventional Gibbs sampling schemes for LDA require O(K) operations per sample where K is the number of topics in the model. Our proposed method draws equivalent samples but requires on average significantly less then K operations per sample. On real-word corpora FastLDA can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA. No approximations are necessary, and we show that our fast sampling scheme produces exactly the same results as the standard (but slower) sampling scheme. Experiments on four real world data sets demonstrate speedups for a wide range of collection sizes. For the PubMed collection of over 8 million documents with a required computation time of 6 CPU months for LDA, our speedup of 5.7 can save 5 CPU months of computation.

 processors only sees We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed compu- of the total data set. We pro- tation, where each of pose two distributed inference schemes that are motivated from different perspec- tives. The first scheme uses local Gibbs sampling on each processor with periodic updates—it is simple to implement and can be viewed as an approximation to a single processor implementation of Gibbs sampling. The second scheme re- lies on a hierarchical Bayesian extension of the standard LDA model to directly processors—it has a theo- account for the fact that data are distributed across retical guarantee of convergence but is more complex to implement than the ap- proximate method. Using five real-world text corpora we show that distributed learning works very well for LDA models, i.e., perplexity and precision-recall scores for distributed learning are indistinguishable from those obtained with single-processor learning. Our extensive experimental results include large-scale distributed computation on 1000 virtual processors; and speedup experiments of learning topics in a 100-million word corpus using 16 processors.

The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do not explicitly address the textual interactions between who/where, i.e. named entities (persons, organizations, locations) and what, i.e. the topics. We present new graphical models that directly learn the relationship between topics discussed in news articles and entities mentioned in each article. We show how these entity-topic models, through a better understanding of the entity-topic relationships, are better at making predictions about entities.

Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference pro- cedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA, and show that it is computationally efficient, easy to implement and signifi- cantly more accurate than standard variational Bayesian inference for LDA.

Abstract We use a probabilistic mixture decomposition method to determine topics in thePennsylvania Gazette, a major colonial U.S. newspaper from 1728–1800. We assess the value of several topic decomposition techniques for historical research and compare the accuracy and efficacy of various methods. After determining the topics covered by the 80,000 articles and advertisements in the entire 18th century run of theGazette, we calculate how the prevalence of those topics changed over time, and give historically relevant examples of our findings. This approach reveals important information about the content of this colonial newspaper, and suggests the value of such approaches to a more complete understanding of early American print culture and society.