Probabilistic topic models Topic modeling provides methods for automatically organizing, understanding, searching, and summarizing large electronic archives. Topic modeling sits in the larger field of probabilistic modeling, a field that has great potential for the humanities. Note that this latter analysis factors out other topics (such as film) from each text in order to focus on the topic of interest. By DaviD m. Blei Probabilistic topic models as OUr COLLeCTive knowledge continues to be digitized and stored—in the form of news, blogs, Web pages, scientific articles, books, images, sound, video, and social networks—it becomes more difficult to find and discover what we are looking for. A topic model takes a collection of texts as input. Berkeley Computer Science. The humanities, fields where questions about texts are paramount, is an ideal testbed for topic modeling and fertile ground for interdisciplinary collaborations with computer scientists and statisticians. david.blei@columbia.edu Abstract Topic modeling analyzes documents to learn meaningful patterns of words. Hongbo Dong; A New Approach to Relax Nonconvex Quadratics. Below, you will find links to introductory materials and opensource software (from my research group) for topic modeling. As examples, we have developed topic models that include syntax, topic hierarchies, document networks, topics drifting through time, readers’ libraries, and the influence of past articles on future articles. Topic Modeling Workshop: Mimno from MITH in MD on Vimeo.. about gibbs sampling starting at minute XXX. Traditionally, statistics and machine learning gives a “cookbook” of methods, and users of these tools are required to match their specific problems to general solutions. David M. Blei is an associate professor of Computer Science at Princeton University. More broadly, topic modeling is a case study in the large field of applied probabilistic modeling. 2 Andrew Polar, November 23, 2011 at 5:44 p.m.: Correlated Topic Models. The goal is for scholars and scientists to creatively design models with an intuitive language of components, and then for computer programs to derive and execute the corresponding inference algorithms with real data. In International Conference on Machine Learning (2006), ACM, New York, NY, USA, 113--120. Researchers have developed fast algorithms for discovering topics; the analysis of of 1.8 million articles in Figure 1 took only a few hours on a single computer. Abstract: Probabilistic topic models provide a suite of tools for analyzing large document collections. But the results are not.. And what we put into the process, neither!. His research focuses on probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. Figure 1 illustrates topics found by running a topic model on 1.8 million articles from the New York Times. For example, we can isolate a subset of texts based on which combination of topics they exhibit (such as film and politics). Both of these analyses require that we know the topics and which topics each document is about. Bio: David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. ... Collaborative topic modeling for recommending scientific articles. Dynamic topic models. David Blei's main research interest lies in the fields of machine learning and Bayesian statistics. Topic modeling is a catchall term for a group of computational techniques that, at a very high level, find patterns of co-occurrence in data (broadly conceived). Topic models are a suite of algorithms that uncover the hiddenthematic structure in document collections. His research focuses on probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. History. John Lafferty, David Blei. [4] I emphasize that this is a conceptual process. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. Dynamic topic models. LDA is an example of a topic model and belongs to the machine learning toolbox and in wider sense to the artificial intelligence toolbox. As I have mentioned, topic models find the sets of terms that tend to occur together in the texts. They analyze the texts to find a set of topics — patterns of tightly co-occurring terms — and how each document combines them. Hierarchically Supervised Latent Dirichlet Allocation. We type keywords into a search engine and find a set of documents related to them. Finally, she uses those estimates in subsequent study, trying to confirm her theories, forming new theories, and using the discovered structure as a lens for exploration. For example, we can identify articles important within a field and articles that transcend disciplinary boundaries. He works on a variety of applications, including text, images, music, social networks, and various scientific data. Then, for each document, choose topic weights to describe which topics that document is about. David M. Blei Topic modeling analyzes documents to learn meaningful patterns of words. What does this have to do with the humanities? Professor of Statistics and Computer Science, Columbia University. I will then discuss the broader field of probabilistic modeling, which gives a flexible language for expressing assumptions about data and a set of algorithms for computing under those assumptions. 1 2 3 Discover the hidden themes that pervade the collection. In this essay I will discuss topic models and how they relate to digital humanities. Topic Models David M. Blei Department of Computer Science Princeton University September 1, 2009 D. Blei Topic Models Among these algorithms, Latent Dirichlet Allocation (LDA), a technique based in Bayesian Modeling, is the most commonly used nowadays. In probabilistic modeling, we provide a language for expressing assumptions about data and generic methods for computing with those assumptions. David Blei. Each led to new kinds of inferences and new ways of visualizing and navigating texts. The generative process for LDA is as follows. How-ever, existing topic models fail to learn inter-pretable topics when working with large and heavy-tailed vocabularies. The topics are distributions over terms in the vocabulary; the document weights are distributions over topics. The inference algorithm (like the one that produced Figure 1) finds the topics that best describe the collection under these assumptions. [3], In particular, LDA is a type of probabilistic model with hidden variables. Speakers David Blei. David Blei is a Professor of Statistics and Computer Science at Columbia University. With the model and the archive in place, she then runs an algorithm to estimate how the imagined hidden structure is realized in actual texts. A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. ), Distributions must sum to one. Imagine searching and exploring documents based on the themes that run through them. In particular, both the topics and the document weights are probability distributions. Topic modeling can be used to help explore, summarize, and form predictions about documents. Each of these projects involved positing a new kind of topical structure, embedding it in a generative process of documents, and deriving the corresponding inference algorithm to discover that structure in real collections. 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