Models, Inference, and Implementation for Scalable Probabilistic Models of Text

Unsupervised probabilistic Bayesian models are powerful tools for statistical analysis, especially in the area of information retrieval, document analysis and text processing. Despite their success, unsupervised probabilistic Bayesian models are often slow in inference due to inter-entangled mutually dependent latent variables. In addition, the parameter space of these models is usually very large. As the data from various different media sources--for example, internet, electronic books, digital films, etc--become widely accessible, lack of scalability for these unsupervised probabilistic Bayesian models becomes a critical bottleneck. The primary focus of this dissertation is to speed up the inference process in unsupervised probabilistic Bayesian models. There are two common solutions to scale the algorithm up to large data: parallelization or streaming. The former achieves scalability by distributing the data and the computation to multiple machines. The latter assumes data come in a stream and updates the model gradually after seeing each data observation. It is able to scale to larger datasets because it usually takes only one pass over the entire data. In this dissertation, we examine both approaches. We first demonstrate the effectiveness of the parallelization approach on a class of unsupervised Bayesian models--topic models, which are exemplified by latent Dirichlet allocation (LDA). We propose a fast parallel implementation using variational inference on the MapRe- duce framework, referred to as Mr. LDA. We show that parallelization enables topic models to handle significantly larger datasets. We further show that our implementation--unlike highly tuned and specialized implementations--is easily extensible. We demonstrate two extensions possible with this scalable framework: 1) informed priors to guide topic discovery and 2) extracting topics from a multilingual corpus. We propose polylingual tree-based topic models to infer topics in multilingual corpora. We then propose three different inference methods to infer the latent variables. We examine the effectiveness of different inference methods on the task of machine translation in which we use the proposed model to extract domain knowledge that considers both source and target languages. We apply it on a large collection of aligned Chinese-English sentences and show that our model yields significant improvement on BLEU score over strong baselines. Other than parallelization, another approach to deal with scalability is to learn parameters in an online streaming setting. Although many online algorithms have been proposed for LDA, they all overlook a fundamental but challenging problem-- the vocabulary is constantly evolving over time. To address this problem, we propose an online LDA with infinite vocabulary--infvoc LDA. We derive online hybrid inference for our model and propose heuristics to dynamically order, expand, and contract the set of words in our vocabulary. We show that our algorithm is able to discover better topics by incorporating new words into the vocabulary and constantly refining the topics over time. In addition to LDA, we also show generality of the online hybrid inference framework by applying it to adaptor grammars, which are a broader class of models subsuming LDA. With proper grammar rules, it simplifies to the exact LDA model, however, it provides more flexibility to alter or extend LDA with different grammar rules. We develop online hybrid inference for adaptor grammar, and show that our method discovers high-quality structure more quickly than both MCMC and variational inference methods

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Rich Linguistic Structure from Large-Scale Web Data

The past two decades have shown an unexpected effectiveness of Web-scale data in natural language processing. Even the simplest models, when paired with unprecedented amounts of unstructured and unlabeled Web data, have been shown to outperform sophisticated ones. It has been argued that the effectiveness of Web-scale data has undermined the necessity of sophisticated modeling or laborious data set curation. In this thesis, we argue for and illustrate an alternative view, that Web-scale data not only serves to improve the performance of simple models, but also can allow the use of qualitatively more sophisticated models that would not be deployable otherwise, leading to even further performance gains.Engineering and Applied Science

Probabilistic latent variable models for knowledge discovery and optimization

I conduct a systematic study of probabilistic latent variable models (PLVMs) with applications to knowledge discovery and optimization. Probabilistic modeling is a principled means to gain insight of data. By assuming that the observed data are generated from a distribution, we can estimate its density, or the statistics of our interest, by either Maximum Likelihood Estimation or Bayesian inference, depending on whether there is a prior distribution for the parameters of the assumed data distribution. One of the primary goals of various machine learning/data mining models is to reveal the underlying knowledge of observed data. A common practice is to introduce latent variables, which are modeled together with the observations. Such latent variables compute, for example, the class assignments (labels), the cluster membership, as well as other unobserved measurements of the data. Besides, proper exploitation of latent variables facilities the optimization itself, which leads to computationally efficient inference algorithms. In this thesis, I describe a range of applications where latent variables can be leveraged for knowledge discovery and efficient optimization. Works in this thesis demonstrate that PLVMs are a powerful tool for modeling incomplete observations. Through incorporating latent variables and assuming that the observations such as citations, pairwise preferences as well as text are generated following tractable distributions parametrized by the latent variables, PLVMs are flexible and effective to discover knowledge in data mining problems, where the knowledge is mathematically modelled as continuous or discrete values, distributions or uncertainty. In addition, I also explore PLVMs for deriving efficient algorithms. It has been shown that latent variables can be employed as a means for model reduction and facilitates the computation/sampling of intractable distributions. Our results lead to algorithms which take advantage of latent variables in probabilistic models. We conduct experiments against state-of-the-art models and empirical evaluation shows that our proposed approaches improve both learning performance and computational efficiency

Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges

Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms performance is greatly influenced by the representations that are derived from the data in the learning process. The representations learned in a successful learning process should be concise, discrete, meaningful, and able to be applied across a variety of tasks. A recent effort has been directed toward developing Deep Learning models, which have proven to be particularly effective at capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we discuss the principles and developments that have been made in the process of learning representations, and converting them into desirable applications. In addition, for each framework or model, the key issues and open challenges, as well as the advantages, are examined