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arxiv_topic_modeling

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Histogram Meets Topic Model: Density Estimation by Mixture of Histograms

Hideaki Kim, Hiroshi Sawada

The histogram method is a powerful non-parametric approach for estimating the probability density function of a continuous variable. But the construction of a histogram, compared to the parametric approaches, demands a large number of observations to capture the underlying density function. Thus it is not suitable for analyzing a sparse data set, a collection of units with a small size of data. In this paper, by employing the probabilistic topic model, we develop a novel Bayesian approach to alleviating the sparsity problem in the conventional histogram estimation. Our method estimates a unit's density function as a mixture of basis histograms, in which the number of bins for each basis, as well as their heights, is determined automatically. The estimation procedure is performed by using the fast and easy-to-implement collapsed Gibbs sampling. We apply the proposed method to synthetic data, showing that it performs well.

http://arxiv.org/abs/1512.07960v1

stat.ML

ATD: Anomalous Topic Discovery in High Dimensional Discrete Data

Hossein Soleimani, David J. Miller

We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups (clusters) of anomalies; i.e. sets of points which collectively exhibit abnormal patterns. In many applications this can lead to better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies. Moreover, we consider the case where the atypical patterns exhibit on only a small (salient) subset of the very high dimensional feature space. Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features. In this paper, we focus on detecting anomalous topics in a batch of text documents, developing our algorithm based on topic models. Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques. All required code to reproduce our experiments is available from https://github.com/hsoleimani/ATD

http://arxiv.org/abs/1512.06452v2

stat.ML, cs.LG

Towards Evaluation of Cultural-scale Claims in Light of Topic Model Sampling Effects

Jaimie Murdock, Jiaan Zeng, Colin Allen

Cultural-scale models of full text documents are prone to over-interpretation by researchers making unintentionally strong socio-linguistic claims (Pechenick et al., 2015) without recognizing that even large digital libraries are merely samples of all the books ever produced. In this study, we test the sensitivity of the topic models to the sampling process by taking random samples of books in the Hathi Trust Digital Library from different areas of the Library of Congress Classification Outline. For each classification area, we train several topic models over the entire class with different random seeds, generating a set of spanning models. Then, we train topic models on random samples of books from the classification area, generating a set of sample models. Finally, we perform a topic alignment between each pair of models by computing the Jensen-Shannon distance (JSD) between the word probability distributions for each topic. We take two measures on each model alignment: alignment distance and topic overlap. We find that sample models with a large sample size typically have an alignment distance that falls in the range of the alignment distance between spanning models. Unsurprisingly, as sample size increases, alignment distance decreases. We also find that the topic overlap increases as sample size increases. However, the decomposition of these measures by sample size differs by number of topics and by classification area. We speculate that these measures could be used to find classes which have a common "canon" discussed among all books in the area, as shown by high topic overlap and low alignment distance even in small sample sizes.

http://arxiv.org/abs/1512.05004v3

cs.DL, cs.CL, cs.IR

Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums

Abhimanu Kumar, Shriphani Palakodety, Chong Wang, Carolyn P. Rose, Eric P. Xing, Miaomiao Wen

Online discussion forums are complex webs of overlapping subcommunities (macrolevel structure, across threads) in which users enact different roles depending on which subcommunity they are participating in within a particular time point (microlevel structure, within threads). This sub-network structure is implicit in massive collections of threads. To uncover this structure, we develop a scalable algorithm based on stochastic variational inference and leverage topic models (LDA) along with mixed membership stochastic block (MMSB) models. We evaluate our model on three large-scale datasets, Cancer-ThreadStarter (22K users and 14.4K threads), Cancer-NameMention(15.1K users and 12.4K threads) and StackOverFlow (1.19 million users and 4.55 million threads). Qualitatively, we demonstrate that our model can provide useful explanations of microlevel and macrolevel user presentation characteristics in different communities using the topics discovered from posts. Quantitatively, we show that our model does better than MMSB and LDA in predicting user reply structure within threads. In addition, we demonstrate via synthetic data experiments that the proposed active sub-network discovery model is stable and recovers the original parameters of the experimental setup with high probability.

http://arxiv.org/abs/1512.03443v1

stat.ML, cs.SI

Guaranteed inference in topic models

Khoat Than, Tung Doan

One of the core problems in statistical models is the estimation of a posterior distribution. For topic models, the problem of posterior inference for individual texts is particularly important, especially when dealing with data streams, but is often intractable in the worst case. As a consequence, existing methods for posterior inference are approximate and do not have any guarantee on neither quality nor convergence rate. In this paper, we introduce a provably fast algorithm, namely Online Maximum a Posteriori Estimation (OPE), for posterior inference in topic models. OPE has more attractive properties than existing inference approaches, including theoretical guarantees on quality and fast rate of convergence to a local maximal/stationary point of the inference problem. The discussions about OPE are very general and hence can be easily employed in a wide range of contexts. Finally, we employ OPE to design three methods for learning Latent Dirichlet Allocation from text streams or large corpora. Extensive experiments demonstrate some superior behaviors of OPE and of our new learning methods.

http://arxiv.org/abs/1512.03308v2

stat.ML

Inference in topic models: sparsity and trade-off

Khoat Than, Tu Bao Ho

Topic models are popular for modeling discrete data (e.g., texts, images, videos, links), and provide an efficient way to discover hidden structures/semantics in massive data. One of the core problems in this field is the posterior inference for individual data instances. This problem is particularly important in streaming environments, but is often intractable. In this paper, we investigate the use of the Frank-Wolfe algorithm (FW) for recovering sparse solutions to posterior inference. From detailed elucidation of both theoretical and practical aspects, FW exhibits many interesting properties which are beneficial to topic modeling. We then employ FW to design fast methods, including ML-FW, for learning latent Dirichlet allocation (LDA) at large scales. Extensive experiments show that to reach the same predictiveness level, ML-FW can perform tens to thousand times faster than existing state-of-the-art methods for learning LDA from massive/streaming data.

http://arxiv.org/abs/1512.03300v1

stat.ML

Jointly Modeling Topics and Intents with Global Order Structure

Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.

http://arxiv.org/abs/1512.02009v1

cs.CL, cs.IR, cs.LG

Stochastic Collapsed Variational Inference for Sequential Data

Pengyu Wang, Phil Blunsom

Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm in the sequential data setting. Our algorithm is applicable to both finite hidden Markov models and hierarchical Dirichlet process hidden Markov models, and to any datasets generated by emission distributions in the exponential family. Our experiment results on two discrete datasets show that our inference is both more efficient and more accurate than its uncollapsed version, stochastic variational inference.

http://arxiv.org/abs/1512.01666v1

stat.ML

Stochastic Collapsed Variational Inference for Hidden Markov Models

Pengyu Wang, Phil Blunsom

Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm for hidden Markov models, in a sequential data setting. Given a collapsed hidden Markov Model, we break its long Markov chain into a set of short subchains. We propose a novel sum-product algorithm to update the posteriors of the subchains, taking into account their boundary transitions due to the sequential dependencies. Our experiments on two discrete datasets show that our collapsed algorithm is scalable to very large datasets, memory efficient and significantly more accurate than the existing uncollapsed algorithm.

http://arxiv.org/abs/1512.01665v1

stat.ML

Probabilistic Latent Semantic Analysis (PLSA) untuk Klasifikasi Dokumen Teks Berbahasa Indonesia

Derwin Suhartono

One task that is included in managing documents is how to find substantial information inside. Topic modeling is a technique that has been developed to produce document representation in form of keywords. The keywords will be used in the indexing process and document retrieval as needed by users. In this research, we will discuss specifically about Probabilistic Latent Semantic Analysis (PLSA). It will cover PLSA mechanism which involves Expectation Maximization (EM) as the training algorithm, how to conduct testing, and obtain the accuracy result.

http://arxiv.org/abs/1512.00576v1

cs.CL, cs.IR

Aspect-based Opinion Summarization with Convolutional Neural Networks

Haibing Wu, Yiwei Gu, Shangdi Sun, Xiaodong Gu

This paper considers Aspect-based Opinion Summarization (AOS) of reviews on particular products. To enable real applications, an AOS system needs to address two core subtasks, aspect extraction and sentiment classification. Most existing approaches to aspect extraction, which use linguistic analysis or topic modeling, are general across different products but not precise enough or suitable for particular products. Instead we take a less general but more precise scheme, directly mapping each review sentence into pre-defined aspects. To tackle aspect mapping and sentiment classification, we propose two Convolutional Neural Network (CNN) based methods, cascaded CNN and multitask CNN. Cascaded CNN contains two levels of convolutional networks. Multiple CNNs at level 1 deal with aspect mapping task, and a single CNN at level 2 deals with sentiment classification. Multitask CNN also contains multiple aspect CNNs and a sentiment CNN, but different networks share the same word embeddings. Experimental results indicate that both cascaded and multitask CNNs outperform SVM-based methods by large margins. Multitask CNN generally performs better than cascaded CNN.

http://arxiv.org/abs/1511.09128v1

cs.CL, cs.IR, cs.LG

MOOCs Meet Measurement Theory: A Topic-Modelling Approach

Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, Jeffrey Chan

This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.

http://arxiv.org/abs/1511.07961v1

cs.LG, cs.CY

Topic Model Based Behaviour Modeling and Clustering Analysis for Wireless Network Users

Bingjie Leng, Jingchu Liu, Huimin Pan, Sheng Zhou, Zhisheng Niu

User behaviour analysis based on traffic log in wireless networks can be beneficial to many fields in real life: not only for commercial purposes, but also for improving network service quality and social management. We cluster users into groups marked by the most frequently visited websites to find their preferences. In this paper, we propose a user behaviour model based on Topic Model from document classification problems. We use the logarithmic TF-IDF (term frequency - inverse document frequency) weighing to form a high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic analysis) to deduce the latent topic distribution and generate a low-dimensional dense feature matrix. K-means++, which is a classic clustering algorithm, is then applied to the dense feature matrix and several interpretable user clusters are found. Moreover, by combining the clustering results with additional demographical information, including age, gender, and financial information, we are able to uncover more realistic implications from the clustering results.

http://arxiv.org/abs/1511.05618v1

cs.SI

Topic Modeling of Behavioral Modes Using Sensor Data

Yehezkel S. Resheff, Shay Rotics, Ran Nathan, Daphna Weinshall

The field of Movement Ecology, like so many other fields, is experiencing a period of rapid growth in availability of data. As the volume rises, traditional methods are giving way to machine learning and data science, which are playing an increasingly large part it turning this data into science-driving insights. One rich and interesting source is the bio-logger. These small electronic wearable devices are attached to animals free to roam in their natural habitats, and report back readings from multiple sensors, including GPS and accelerometer bursts. A common use of accelerometer data is for supervised learning of behavioral modes. However, we need unsupervised analysis tools as well, in order to overcome the inherent difficulties of obtaining a labeled dataset, which in some cases is either infeasible or does not successfully encompass the full repertoire of behavioral modes of interest. Here we present a matrix factorization based topic-model method for accelerometer bursts, derived using a linear mixture property of patch features. Our method is validated via comparison to a labeled dataset, and is further compared to standard clustering algorithms.

http://arxiv.org/abs/1511.05082v1

cs.LG

Bayesian Analysis of Dynamic Linear Topic Models

Chris Glynn, Surya T. Tokdar, David L. Banks, Brian Howard

In dynamic topic modeling, the proportional contribution of a topic to a document depends on the temporal dynamics of that topic's overall prevalence in the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by explicitly modeling document level topic proportions with covariates and dynamic structure that includes polynomial trends and periodicity. A Markov Chain Monte Carlo (MCMC) algorithm that utilizes Polya-Gamma data augmentation is developed for posterior inference. Conditional independencies in the model and sampling are made explicit, and our MCMC algorithm is parallelized where possible to allow for inference in large corpora. To address computational bottlenecks associated with Polya-Gamma sampling, we appeal to the Central Limit Theorem to develop a Gaussian approximation to the Polya-Gamma random variable. This approximation is fast and reliable for parameter values relevant in the text mining domain. Our model and inference algorithm are validated with multiple simulation examples, and we consider the application of modeling trends in PubMed abstracts. We demonstrate that sharing information across documents is critical for accurately estimating document-specific topic proportions. We also show that explicitly modeling polynomial and periodic behavior improves our ability to predict topic prevalence at future time points.

http://arxiv.org/abs/1511.03947v1

stat.ML, cs.LG, stat.ME

Hierarchical Latent Semantic Mapping for Automated Topic Generation

Guorui Zhou, Guang Chen

Much of information sits in an unprecedented amount of text data. Managing allocation of these large scale text data is an important problem for many areas. Topic modeling performs well in this problem. The traditional generative models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and most recent research on topic generation has been focusing on improving or extending these models. However, results of traditional generative models are sensitive to the number of topics K, which must be specified manually. The problem of generating topics from corpus resembles community detection in networks. Many effective algorithms can automatically detect communities from networks without a manually specified number of the communities. Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus. HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network. We apply HLSM to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.

http://arxiv.org/abs/1511.03546v4

cs.LG, cs.CL, cs.IR

Co-word Maps and Topic Modeling: A Comparison Using Small and Medium-Sized Corpora (n < 1000)

Loet Leydesdorff, Adina Nerghes

Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n = 687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping in small and medium-sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.

http://arxiv.org/abs/1511.03020v2

cs.DL

Partial Membership Latent Dirichlet Allocation

Chao Chen, Alina Zare, J. Tory Cobb

Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership latent Dirichlet allocation (PM-LDA) model and associated parameter estimation algorithms. Experimental results on two natural image datasets and one SONAR image dataset show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability existing methods do not have.

http://arxiv.org/abs/1511.02821v2

stat.ML, cs.CV

ZenLDA: An Efficient and Scalable Topic Model Training System on Distributed Data-Parallel Platform

Bo Zhao, Hucheng Zhou, Guoqiang Li, Yihua Huang

This paper presents our recent efforts, zenLDA, an efficient and scalable Collapsed Gibbs Sampling system for Latent Dirichlet Allocation training, which is thought to be challenging that both data parallelism and model parallelism are required because of the Big sampling data with up to billions of documents and Big model size with up to trillions of parameters. zenLDA combines both algorithm level improvements and system level optimizations. It first presents a novel CGS algorithm that balances the time complexity, model accuracy and parallelization flexibility. The input corpus in zenLDA is represented as a directed graph and model parameters are annotated as the corresponding vertex attributes. The distributed training is parallelized by partitioning the graph that in each iteration it first applies CGS step for all partitions in parallel, followed by synchronizing the computed model each other. In this way, both data parallelism and model parallelism are achieved by converting them to graph parallelism. We revisited the tradeoff between system efficiency and model accuracy and presented approximations such as unsynchronized model, sparse model initialization and "converged" token exclusion. zenLDA is built on GraphX in Spark that provides distributed data abstraction (RDD) and expressive APIs to simplify the programming efforts and simultaneously hides the system complexities. This enables us to implement other CGS algorithm with a few lines of code change. To better fit in distributed data-parallel framework and achieve comparable performance with contemporary systems, we also presented several system level optimizations to push the performance limit. zenLDA was evaluated it against web-scale corpus, and the result indicates that zenLDA can achieve about much better performance than other CGS algorithm we implemented, and simultaneously achieve better model accuracy.

http://arxiv.org/abs/1511.00440v1

cs.DC

Spatial Semantic Scan: Jointly Detecting Subtle Events and their Spatial Footprint

Abhinav Maurya

Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specifically to overcome the shortcomings of current methods in detecting new spatially compact events in text streams. SCSS employs alternating optimization between using semantic scan to estimate contrastive foreground topics in documents, and discovering spatial neighborhoods with high occurrence of documents containing the foreground topics. We evaluate our method on Emergency Department chief complaints dataset (ED dataset) to verify the effectiveness of our method in detecting real-world disease outbreaks from free-text ED chief complaint data.

http://arxiv.org/abs/1511.00352v3

cs.LG, cs.CL, stat.ML

A 'Gibbs-Newton' Technique for Enhanced Inference of Multivariate Polya Parameters and Topic Models

Osama Khalifa, David Wolfe Corne, Mike Chantler

Hyper-parameters play a major role in the learning and inference process of latent Dirichlet allocation (LDA). In order to begin the LDA latent variables learning process, these hyper-parameters values need to be pre-determined. We propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs Newton' (LDA-GN), which places non-informative priors over these hyper-parameters and uses Gibbs sampling to learn appropriate values for them. At the heart of LDA-GN is our proposed 'Gibbs-Newton' algorithm, which is a new technique for learning the parameters of multivariate Polya distributions. We report Gibbs-Newton performance results compared with two prominent existing approaches to the latter task: Minka's fixed-point iteration method and the Moments method. We then evaluate LDA-GN in two ways: (i) by comparing it with standard LDA in terms of the ability of the resulting topic models to generalize to unseen documents; (ii) by comparing it with standard LDA in its performance on a binary classification task.

http://arxiv.org/abs/1510.06646v2

cs.LG, cs.CL, stat.ML

Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling

Aaron Q Li

There is an explosion of data, documents, and other content, and people require tools to analyze and interpret these, tools to turn the content into information and knowledge. Topic modeling have been developed to solve these problems. Topic models such as LDA [Blei et. al. 2003] allow salient patterns in data to be extracted automatically. When analyzing texts, these patterns are called topics. Among numerous extensions of LDA, few of them can reliably analyze multiple groups of documents and extract topic similarities. Recently, the introduction of differential topic modeling (SPDP) [Chen et. al. 2012] performs uniformly better than many topic models in a discriminative setting. There is also a need to improve the sampling speed for topic models. While some effort has been made for distributed algorithms, there is no work currently done using graphical processing units (GPU). Note the GPU framework has already become the most cost-efficient platform for many problems. In this thesis, I propose and implement a scalable multi-GPU distributed parallel framework which approximates SPDP. Through experiments, I have shown my algorithms have a gain in speed of about 50 times while being almost as accurate, with only one single cheap laptop GPU. Furthermore, I have shown the speed improvement is sublinearly scalable when multiple GPUs are used, while fairly maintaining the accuracy. Therefore on a medium-sized GPU cluster, the speed improvement could potentially reach a factor of a thousand. Note SPDP is just a representative of other extensions of LDA. Although my algorithm is implemented to work with SPDP, it is designed to be a general enough to work with other topic models. The speed-up on smaller collections (i.e., 1000s of documents), means that these more complex LDA extensions could now be done in real-time, thus opening up a new way of using these LDA models in industry.

http://arxiv.org/abs/1510.06549v1

cs.CL, cs.DC, cs.LG

Matching Mechanisms For Real-Time Computational Resource Exchange Markets

Joseph W. Robinson, Aaron Q. Li

In this paper we describe matching mechanisms for a real-time computational resource exchange market, Chital, that incentivizes participating clients to perform computation for their peers in exchange for overall improved performance. The system is designed to discourage dishonest behavior via a credit system, while simultaneously minimizing the use of dedicated computing servers and the number of verifications performed by the administrating servers. We describe the system in the context of a pre-existing system (under development), Vedalia \cite{715Project}, for analyzing and visualizing product reviews, by using machine learning such as topic models. We extend this context to general computing tasks, describe a list of matching algorithms, and evaluate their performance in a simulated environment. In addition, we design a matching algorithm that optimizes the amount of time a participant could save compared to computing a task on their own, and show empirically that this algorithm results in a situation in which it is almost always optimal for a user to join the exchange than do computation alone. Lastly, we use a top-down approach to derive a theoretically near-optimal matching algorithm under certain distributional assumptions on query frequency.

http://arxiv.org/abs/1510.06150v1

cs.GT

Transitions in climate and energy discourse between Hurricanes Katrina and Sandy

Emily M. Cody, Jennie C. Stephens, James P. Bagrow, Peter Sheridan Dodds, Christopher M. Danforth

Although climate change and energy are intricately linked, their explicit connection is not always prominent in public discourse and the media. Disruptive extreme weather events, including hurricanes, focus public attention in new and different ways, offering a unique window of opportunity to analyze how a focusing event influences public discourse. Media coverage of extreme weather events simultaneously shapes and reflects public discourse on climate issues. Here we analyze climate and energy newspaper coverage of Hurricanes Katrina (2005) and Sandy (2012) using topic models, mathematical techniques used to discover abstract topics within a set of documents. Our results demonstrate that post-Katrina media coverage does not contain a climate change topic, and the energy topic is limited to discussion of energy prices, markets, and the economy with almost no explicit linkages made between energy and climate change. In contrast, post-Sandy media coverage does contain a prominent climate change topic, a distinct energy topic, as well as integrated representation of climate change and energy, indicating a shift in climate and energy reporting between Hurricane Katrina and Hurricane Sandy.

http://arxiv.org/abs/1510.07494v2

physics.soc-ph, physics.pop-ph

Large Enforced Sparse Non-Negative Matrix Factorization

Brendan Gavin, Vijay Gadepally, Jeremy Kepner

Non-negative matrix factorization (NMF) is a common method for generating topic models from text data. NMF is widely accepted for producing good results despite its relative simplicity of implementation and ease of computation. One challenge with applying NMF to large datasets is that intermediate matrix products often become dense, stressing the memory and compute elements of a system. In this article, we investigate a simple but powerful modification of a common NMF algorithm that enforces the generation of sparse intermediate and output matrices. This method enables the application of NMF to large datasets through improved memory and compute performance. Further, we demonstrate empirically that this method of enforcing sparsity in the NMF either preserves or improves both the accuracy of the resulting topic model and the convergence rate of the underlying algorithm.

http://arxiv.org/abs/1510.05237v1

cs.LG, cs.NA, cs.SI

A Historical Analysis of the Field of OR/MS using Topic Models

Christopher J. Gatti, James D. Brooks, Sarah G. Nurre

This study investigates the content of the published scientific literature in the fields of operations research and management science (OR/MS) since the early 1950s. Our study is based on 80,757 published journal abstracts from 37 of the leading OR/MS journals. We have developed a topic model, using Latent Dirichlet Allocation (LDA), and extend this analysis to reveal the temporal dynamics of the field, journals, and topics. Our analysis shows the generality or specificity of each of the journals, and we identify groups of journals with similar content, which are both consistent and inconsistent with intuition. We also show how journals have become more or less unique in their scope. A more detailed analysis of each journals' topics over time shows significant temporal dynamics, especially for journals with niche content. This study presents an observational, yet objective, view of the published literature from OR/MS that would be of interest to authors, editors, journals, and publishers. Furthermore, this work can be used by new entrants to the fields of OR/MS to understand the content landscape, as a starting point for discussions and inquiry of the field at large, or as a model for other fields to perform similar analyses.

http://arxiv.org/abs/1510.05154v1

stat.ML, cs.DL, stat.AP

A General Method for Robust Bayesian Modeling

Chong Wang, David M. Blei

Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bursty topic models. In this paper we develop a general approach to robust Bayesian modeling. We show how to turn an existing Bayesian model into a robust model, and then develop a generic strategy for computing with it. We use our method to study robust variants of several models, including linear regression, Poisson regression, logistic regression, and probabilistic topic models. We discuss the connections between our methods and existing approaches, especially empirical Bayes and James-Stein estimation.

http://arxiv.org/abs/1510.05078v3

stat.ML

Partitioning Algorithms for Improving Efficiency of Topic Modeling Parallelization

Hung Nghiep Tran, Atsuhiro Takasu

Topic modeling is a very powerful technique in data analysis and data mining but it is generally slow. Many parallelization approaches have been proposed to speed up the learning process. However, they are usually not very efficient because of the many kinds of overhead, especially the load-balancing problem. We address this problem by proposing three partitioning algorithms, which either run more quickly or achieve better load balance than current partitioning algorithms. These algorithms can easily be extended to improve parallelization efficiency on other topic models similar to LDA, e.g., Bag of Timestamps, which is an extension of LDA with time information. We evaluate these algorithms on two popular datasets, NIPS and NYTimes. We also build a dataset containing over 1,000,000 scientific publications in the computer science domain from 1951 to 2010 to experiment with Bag of Timestamps parallelization, which we design to demonstrate the proposed algorithms' extensibility. The results strongly confirm the advantages of these algorithms.

http://arxiv.org/abs/1510.04317v1

cs.DC

Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates

Stefano Gurciullo, Michael Smallegan, María Pereda, Federico Battiston, Alice Patania, Sebastian Poledna, Daniel Hedblom, Bahattin Tolga Oztan, Alexander Herzog, Peter John, Slava Mikhaylov

This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications.

http://arxiv.org/abs/1510.03797v1

physics.soc-ph, cs.CL, cs.SI, 91F10

A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization

Ramakrishnan Kannan, Grey Ballard, Haesun Park

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors $W$ and $H$, for the given input matrix $A$, such that $A \approx W H$. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel software to solve the problem for big datasets. Existing distributed-memory algorithms are limited in terms of performance and applicability, as they are implemented using Hadoop and are designed only for sparse matrices. We propose a distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems for $W$ and $H$. To our knowledge, our algorithm is the first high-performance parallel algorithm for NMF. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementations, our algorithm is also flexible: (1) it performs well for dense and sparse matrices, and (2) it allows the user to choose from among multiple algorithms for solving local NLS subproblems within the alternating iterations. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements.

http://arxiv.org/abs/1509.09313v1

cs.DC

Asynchronous Gibbs Sampling

Alexander Terenin, Daniel Simpson, David Draper

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. MCMC methods can be difficult to deploy on parallel and distributed systems due to their inherently sequential nature. We study asynchronous Gibbs sampling, which achieves parallelism by simply ignoring sequential requirements. This method has been shown to produce good empirical results for some hierarchical models, and is popular in the topic modeling community, but was also shown to diverge for other targets. We introduce a theoretical framework for analyzing asynchronous Gibbs sampling and other extensions of MCMC that do not possess the Markov property. We prove that asynchronous Gibbs can be modified so that it converges under appropriate regularity conditions -- we call this the exact asynchronous Gibbs algorithm. We study asynchronous Gibbs on a set of examples by comparing the exact and approximate algorithms, including two where it works well, and one where it fails dramatically. We conclude with a set of heuristics to describe settings where the algorithm can be effectively used.

http://arxiv.org/abs/1509.08999v7

stat.CO

Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling

Yogesh Girdhar, Walter Cho, Matthew Campbell, Jesus Pineda, Elizabeth Clarke, Hanumant Singh

This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. In the second dataset, consisting of video data from a static seafloor camera capturing images of a busy coral reef, the proposed technique was able to detect all three instances of an underwater vehicle passing in front of the camera, amongst many other observations of fishes, debris, lighting changes due to surface waves, and benthic flora.

http://arxiv.org/abs/1509.07979v2

cs.CV, cs.RO

Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring

Yogesh Girdhar, Gregory Dudek

This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We validate the approach using simulated exploration experiments using aerial and underwater data, and demonstrate an implementation on the Aqua underwater robot in a variety of scenarios. We find that the proposed exploration paths that are biased towards locations with high topic perplexity, produce better terrain models with high discriminative power. Moreover, we show that the proposed algorithm implemented on Aqua robot is able to do tasks such as coral reef inspection, diver following, and sea floor exploration, without any prior training or preparation.

http://arxiv.org/abs/1509.07975v1

cs.RO, cs.CV, cs.LG

Bilingual Distributed Word Representations from Document-Aligned Comparable Data

Ivan Vulić, Marie-Francine Moens

We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and context-counting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.

http://arxiv.org/abs/1509.07308v2

cs.CL

Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks

Jaimie Murdock, Colin Allen, Simon DeDeo

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.

http://arxiv.org/abs/1509.07175v5

cs.CL, cs.AI, cs.CY, cs.DL, physics.soc-ph

Gibbs Sampling Strategies for Semantic Perception of Streaming Video Data

Yogesh Girdhar, Gregory Dudek

Topic modeling of streaming sensor data can be used for high level perception of the environment by a mobile robot. In this paper we compare various Gibbs sampling strategies for topic modeling of streaming spatiotemporal data, such as video captured by a mobile robot. Compared to previous work on online topic modeling, such as o-LDA and incremental LDA, we show that the proposed technique results in lower online and final perplexity, given the realtime constraints.

http://arxiv.org/abs/1509.03242v1

cs.RO, cs.LG

On collapsed representation of hierarchical Completely Random Measures

Gaurav Pandey, Ambedkar Dukkipati

The aim of the paper is to provide an exact approach for generating a Poisson process sampled from a hierarchical CRM, without having to instantiate the infinitely many atoms of the random measures. We use completely random measures~(CRM) and hierarchical CRM to define a prior for Poisson processes. We derive the marginal distribution of the resultant point process, when the underlying CRM is marginalized out. Using well known properties unique to Poisson processes, we were able to derive an exact approach for instantiating a Poisson process with a hierarchical CRM prior. Furthermore, we derive Gibbs sampling strategies for hierarchical CRM models based on Chinese restaurant franchise sampling scheme. As an example, we present the sum of generalized gamma process (SGGP), and show its application in topic-modelling. We show that one can determine the power-law behaviour of the topics and words in a Bayesian fashion, by defining a prior on the parameters of SGGP.

http://arxiv.org/abs/1509.01817v2

math.ST, cs.LG, stat.TH

Fast Clustering and Topic Modeling Based on Rank-2 Nonnegative Matrix Factorization

Da Kuang, Barry Drake, Haesun Park

The importance of unsupervised clustering and topic modeling is well recognized with ever-increasing volumes of text data. In this paper, we propose a fast method for hierarchical clustering and topic modeling called HierNMF2. Our method is based on fast Rank-2 nonnegative matrix factorization (NMF) that performs binary clustering and an efficient node splitting rule. Further utilizing the final leaf nodes generated in HierNMF2 and the idea of nonnegative least squares fitting, we propose a new clustering/topic modeling method called FlatNMF2 that recovers a flat clustering/topic modeling result in a very simple yet significantly more effective way than any other existing methods. We implement highly optimized open source software in C++ for both HierNMF2 and FlatNMF2 for hierarchical and partitional clustering/topic modeling of document data sets. Substantial experimental tests are presented that illustrate significant improvements both in computational time as well as quality of solutions. We compare our methods to other clustering methods including K-means, standard NMF, and CLUTO, and also topic modeling methods including latent Dirichlet allocation (LDA) and recently proposed algorithms for NMF with separability constraints. Overall, we present efficient tools for analyzing large-scale data sets, and techniques that can be generalized to many other data analytics problem domains.

http://arxiv.org/abs/1509.01208v3

cs.LG, cs.IR, cs.NA, F.2.1; H.3.3

Nested Hierarchical Dirichlet Processes for Multi-Level Non-Parametric Admixture Modeling

Lavanya Sita Tekumalla, Priyanka Agrawal, Indrajit Bhattacharya

Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixture over shared mixture densities. The Nested Dirichlet Process (nDP), on the other hand, is an extension of the DP for learning group level distributions from data, simultaneously clustering the groups. It allows group level distributions to be shared across groups in a non-parametric setting, leading to a non-parametric mixture of mixtures. The nCRF extends the nDP for multilevel non-parametric mixture modeling, enabling modeling topic hierarchies. However, the nDP and nCRF do not allow sharing of distributions as required in many applications, motivating the need for multi-level non-parametric admixture modeling. We address this gap by proposing multi-level nested HDPs (nHDP) where the base distribution of the HDP is itself a HDP at each level thereby leading to admixtures of admixtures at each level. Because of couplings between various HDP levels, scaling up is naturally a challenge during inference. We propose a multi-level nested Chinese Restaurant Franchise (nCRF) representation for the nested HDP, with which we outline an inference algorithm based on Gibbs Sampling. We evaluate our model with the two level nHDP for non-parametric entity topic modeling where an inner HDP creates a countably infinite set of topic mixtures and associates them with author entities, while an outer HDP associates documents with these author entities. In our experiments on two real world research corpora, the nHDP is able to generalize significantly better than existing models and detect missing author entities with a reasonable level of accuracy.

http://arxiv.org/abs/1508.06446v2

stat.ML, cs.LG

Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery

Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in which each shared latent factor contains a novel word that is unique to that factor, a property that has come to be known as separability. Our algorithm is based on the key insight that the novel words correspond to the extreme points of the convex hull formed by the row-vectors of a suitably normalized word co-occurrence matrix. We leverage this geometric insight to establish polynomial computation and sample complexity bounds based on a few isotropic random projections of the rows of the normalized word co-occurrence matrix. Our proposed random-projections-based algorithm is naturally amenable to an efficient distributed implementation and is attractive for modern web-scale distributed data mining applications.

http://arxiv.org/abs/1508.05565v2

cs.LG, cs.CL, cs.IR, stat.ML

Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections

Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So

Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models. This paper introduces two probabilistic topic models, Correlated LDA (C-LDA) and Correlated HDP (C-HDP). These address problems that can arise when analyzing large, asymmetric, and potentially weakly-related collections. Topic correlations in weakly-related collections typically lie in the tail of the topic distribution, where they would be overlooked by models unable to fit large numbers of topics. To efficiently model this long tail for large-scale analysis, our models implement a parallel sampling algorithm based on the Metropolis-Hastings and alias methods (Yuan et al., 2015). The models are first evaluated on synthetic data, generated to simulate various collection-level asymmetries. We then present a case study of modeling over 300k documents in collections of sciences and humanities research from JSTOR.

http://arxiv.org/abs/1508.04562v1

cs.CL, cs.IR

Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data

Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin

We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a reparameterization of the Poisson distribution as a multinomial facilitates conjugacy in the model and enables simple and efficient Gibbs sampling and variational Bayes (VB) inference updates, with a computational cost that only depends on the number of nonzeros in the tensor. The model also provides a nice interpretability for the factors; in our model, each factor corresponds to a "topic". We develop a set of online inference algorithms that allow further scaling up the model to massive tensors, for which batch inference methods may be infeasible. We apply our framework on diverse real-world applications, such as \emph{multiway} topic modeling on a scientific publications database, analyzing a political science data set, and analyzing a massive household transactions data set.

http://arxiv.org/abs/1508.04211v1

stat.ML, cs.LG

Is That Twitter Hashtag Worth Reading

Anusha A., Sanjay Singh

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.

http://arxiv.org/abs/1508.03607v1

cs.SI, H.5.3; I.2.6; I.2.7

Is Stack Overflow Overflowing With Questions and Tags

Ranjitha R. K., Sanjay Singh

Programming question and answer (Q & A) websites, such as Quora, Stack Overflow, and Yahoo! Answer etc. helps us to understand the programming concepts easily and quickly in a way that has been tested and applied by many software developers. Stack Overflow is one of the most frequently used programming Q\&A website where the questions and answers posted are presently analyzed manually, which requires a huge amount of time and resource. To save the effort, we present a topic modeling based technique to analyze the words of the original texts to discover the themes that run through them. We also propose a method to automate the process of reviewing the quality of questions on Stack Overflow dataset in order to avoid ballooning the stack overflow with insignificant questions. The proposed method also recommends the appropriate tags for the new post, which averts the creation of unnecessary tags on Stack Overflow.

http://arxiv.org/abs/1508.03601v1

cs.SI, cs.CL, H.5.3; I.2.6; I.2.7

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for maximum a posterior inference and (ii) back propagation over a deep architecture together with stochastic gradient/mirror descent for model parameter estimation, leading to scalable and end-to-end discriminative learning of the model. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model (i.e., BP-LDA). Experimental results on three real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised topic models, neural networks, and is on par with deep neural networks.

http://arxiv.org/abs/1508.03398v2

cs.LG

Topic Stability over Noisy Sources

Jing Su, Oisín Boydell, Derek Greene, Gerard Lynch

Topic modelling techniques such as LDA have recently been applied to speech transcripts and OCR output. These corpora may contain noisy or erroneous texts which may undermine topic stability. Therefore, it is important to know how well a topic modelling algorithm will perform when applied to noisy data. In this paper we show that different types of textual noise will have diverse effects on the stability of different topic models. From these observations, we propose guidelines for text corpus generation, with a focus on automatic speech transcription. We also suggest topic model selection methods for noisy corpora.

http://arxiv.org/abs/1508.01067v1

cs.CL, cs.IR

Tag-Weighted Topic Model For Large-scale Semi-Structured Documents

Shuangyin Li, Jiefei Li, Guan Huang, Ruiyang Tan, Rong Pan

To date, there have been massive Semi-Structured Documents (SSDs) during the evolution of the Internet. These SSDs contain both unstructured features (e.g., plain text) and metadata (e.g., tags). Most previous works focused on modeling the unstructured text, and recently, some other methods have been proposed to model the unstructured text with specific tags. To build a general model for SSDs remains an important problem in terms of both model fitness and efficiency. We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM). TWTM is a framework that leverages both the tags and words information, not only to learn the document-topic and topic-word distributions, but also to infer the tag-topic distributions for text mining tasks. We present an efficient variational inference method with an EM algorithm for estimating the model parameters. Meanwhile, we propose three large-scale solutions for our model under the MapReduce distributed computing platform for modeling large-scale SSDs. The experimental results show the effectiveness, efficiency and the robustness by comparing our model with the state-of-the-art methods in document modeling, tags prediction and text classification. We also show the performance of the three distributed solutions in terms of time and accuracy on document modeling.

http://arxiv.org/abs/1507.08396v1

cs.CL, cs.IR, cs.LG, stat.ML

The Polylingual Labeled Topic Model

Lisa Posch, Arnim Bleier, Philipp Schaer, Markus Strohmaier

In this paper, we present the Polylingual Labeled Topic Model, a model which combines the characteristics of the existing Polylingual Topic Model and Labeled LDA. The model accounts for multiple languages with separate topic distributions for each language while restricting the permitted topics of a document to a set of predefined labels. We explore the properties of the model in a two-language setting on a dataset from the social science domain. Our experiments show that our model outperforms LDA and Labeled LDA in terms of their held-out perplexity and that it produces semantically coherent topics which are well interpretable by human subjects.

http://arxiv.org/abs/1507.06829v1

cs.CL, cs.IR, cs.LG, G.3; I.2.7

LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

Ashwinkumar Ganesan, Kiante Brantley, Shimei Pan, Jian Chen

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.

http://arxiv.org/abs/1507.06593v1

cs.IR, cs.HC

Exploratory topic modeling with distributional semantics

Samuel Rönnqvist

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.

http://arxiv.org/abs/1507.04798v1

cs.IR, cs.CL, cs.LG

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.

http://arxiv.org/abs/1507.02973v1

cs.SI

Inferring Networks of Substitutable and Complementary Products

Julian McAuley, Rahul Pandey, Jure Leskovec

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.

http://arxiv.org/abs/1506.08839v1

cs.SI, cs.IR

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

Amar Shah, David A. Knowles, Zoubin Ghahramani

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performance of these methods has been assessed primarily in the context of Bayesian topic models, particularly latent Dirichlet allocation (LDA). Deriving several new algorithms, and using synthetic, image and genomic datasets, we investigate whether the understanding gleaned from LDA applies in the setting of sparse latent factor models, specifically beta process factor analysis (BPFA). We demonstrate that the big picture is consistent: using Gibbs sampling within SVI to maintain certain posterior dependencies is extremely effective. However, we find that different posterior dependencies are important in BPFA relative to LDA. Particularly, approximations able to model intra-local variable dependence perform best.

http://arxiv.org/abs/1506.08180v1

stat.ML, cs.LG, stat.AP, stat.CO, stat.ME

Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms

Yuchen Zhang, Michael I. Jordan

Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems. Splash consists of a programming interface and an execution engine. Using the programming interface, the user develops sequential stochastic algorithms without concerning any detail about distributed computing. The algorithm is then automatically parallelized by a communication-efficient execution engine. We provide theoretical justifications on the optimal rate of convergence for parallelizing stochastic gradient descent. Splash is built on top of Apache Spark. The real-data experiments on logistic regression, collaborative filtering and topic modeling verify that Splash yields order-of-magnitude speedup over single-thread stochastic algorithms and over state-of-the-art implementations on Spark.

http://arxiv.org/abs/1506.07552v2

cs.LG

Efficient Learning for Undirected Topic Models

Jiatao Gu, Victor O. K. Li

Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.

http://arxiv.org/abs/1506.07477v1

cs.LG, cs.CL, cs.IR, stat.ML

Sampling constrained probability distributions using Spherical Augmentation

Shiwei Lan, Babak Shahbaba

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.

http://arxiv.org/abs/1506.05936v1

stat.CO, stat.ML

Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture

Ubai Sandouk, Ke Chen

One of the biggest challenges in Multimedia information retrieval and understanding is to bridge the semantic gap by properly modeling concept semantics in context. The presence of out of vocabulary (OOV) concepts exacerbates this difficulty. To address the semantic gap issues, we formulate a problem on learning contextualized semantics from descriptive terms and propose a novel Siamese architecture to model the contextualized semantics from descriptive terms. By means of pattern aggregation and probabilistic topic models, our Siamese architecture captures contextualized semantics from the co-occurring descriptive terms via unsupervised learning, which leads to a concept embedding space of the terms in context. Furthermore, the co-occurring OOV concepts can be easily represented in the learnt concept embedding space. The main properties of the concept embedding space are demonstrated via visualization. Using various settings in semantic priming, we have carried out a thorough evaluation by comparing our approach to a number of state-of-the-art methods on six annotation corpora in different domains, i.e., MagTag5K, CAL500 and Million Song Dataset in the music domain as well as Corel5K, LabelMe and SUNDatabase in the image domain. Experimental results on semantic priming suggest that our approach outperforms those state-of-the-art methods considerably in various aspects.

http://arxiv.org/abs/1506.05514v1

cs.IR, cs.CL, cs.LG, I.2.6

Fast and Guaranteed Tensor Decomposition via Sketching

Yining Wang, Hsiao-Yu Tung, Alexander Smola, Animashree Anandkumar

Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent variable models and in data mining. In this paper, we propose fast and randomized tensor CP decomposition algorithms based on sketching. We build on the idea of count sketches, but introduce many novel ideas which are unique to tensors. We develop novel methods for randomized computation of tensor contractions via FFTs, without explicitly forming the tensors. Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares. We also design novel colliding hashes for symmetric tensors to further save time in computing the sketches. We then combine these sketching ideas with existing whitening and tensor power iterative techniques to obtain the fastest algorithm on both sparse and dense tensors. The quality of approximation under our method does not depend on properties such as sparsity, uniformity of elements, etc. We apply the method for topic modeling and obtain competitive results.

http://arxiv.org/abs/1506.04448v2

stat.ML, cs.LG

Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models

Måns Magnusson, Leif Jonsson, Mattias Villani, David Broman

Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler.

http://arxiv.org/abs/1506.03784v3

stat.ML, stat.ME

Convolutional Dictionary Learning through Tensor Factorization

Furong Huang, Animashree Anandkumar

Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.

http://arxiv.org/abs/1506.03509v3

cs.LG, stat.ML

Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering

Borja Navarro-Colorado, Estela Saquete

Building unified timelines from a collection of written news articles requires cross-document event coreference resolution and temporal relation extraction. In this paper we present an approach event coreference resolution according to: a) similar temporal information, and b) similar semantic arguments. Temporal information is detected using an automatic temporal information system (TIPSem), while semantic information is represented by means of LDA Topic Modeling. The evaluation of our approach shows that it obtains the highest Micro-average F-score results in the SemEval2015 Task 4: TimeLine: Cross-Document Event Ordering (25.36\% for TrackB, 23.15\% for SubtrackB), with an improvement of up to 6\% in comparison to the other systems. However, our experiment also showed some draw-backs in the Topic Modeling approach that degrades performance of the system.

http://arxiv.org/abs/1506.03257v1

cs.CL

Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis

Derek Greene, James P. Cross

This study analyzes political interactions in the European Parliament (EP) by considering how the political agenda of the plenary sessions has evolved over time and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making Parliamentary speeches. It does so by considering the context in which speeches are made, and the content of those speeches. To detect latent themes in legislative speeches over time, speech content is analyzed using a new dynamic topic modeling method, based on two layers of matrix factorization. This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999-2014. Our findings suggest that the political agenda of the EP has evolved significantly over time, is impacted upon by the committee structure of the Parliament, and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro-crisis have a significant impact on what is being discussed in Parliament.

http://arxiv.org/abs/1505.07302v4

cs.CL, cs.CY

Colors $-$Messengers of Concepts: Visual Design Mining for Learning Color Semantics

Ali Jahanian, S. V. N. Vishwanathan, Jan P. Allebach

This paper studies the concept of color semantics by modeling a dataset of magazine cover designs, evaluating the model via crowdsourcing, and demonstrating several prototypes that facilitate color-related design tasks. We investigate a probabilistic generative modeling framework that expresses semantic concepts as a combination of color and word distributions $-$color-word topics. We adopt an extension to Latent Dirichlet Allocation (LDA) topic modeling called LDA-dual to infer a set of color-word topics over a corpus of 2,654 magazine covers spanning 71 distinct titles and 12 genres. While LDA models text documents as distributions over word topics, we model magazine covers as distributions over color-word topics. The results of our crowdsourced experiments confirm that the model is able to successfully discover the associations between colors and linguistic concepts. Finally, we demonstrate several simple prototypes that apply the learned model to color palette recommendation, design example retrieval, image retrieval, image color selection, and image recoloring.

http://arxiv.org/abs/1505.06532v1

cs.HC, H.1.2; H.5.2

Location Prediction of Social Images via Generative Model

Xiaoming Zhang, Zhoujun Li, Senzhang Wang, Yang Yang, Xueqiang Lv

The vast amount of geo-tagged social images has attracted great attention in research of predicting location using the plentiful content of images, such as visual content and textual description. Most of the existing researches use the text-based or vision-based method to predict location. There still exists a problem: how to effectively exploit the correlation between different types of content as well as their geographical distributions for location prediction. In this paper, we propose to predict image location by learning the latent relation between geographical location and multiple types of image content. In particularly, we propose a geographical topic model GTMI (geographical topic model of social image) to integrate multiple types of image content as well as the geographical distributions, In GTMI, image topic is modeled on both text vocabulary and visual feature. Each region has its own distribution over topics and hence has its own language model and vision pattern. The location of a new image is estimated based on the joint probability of image content and similarity measure on topic distribution between images. Experiment results demonstrate the performance of location prediction based on GTMI.

http://arxiv.org/abs/1505.03984v1

cs.IR, H.3.1

Detecting Concept-level Emotion Cause in Microblogging

Shuangyong Song, Yao Meng

In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users' diversified emotions on specific hot event. A modified topic-supervised biterm topic model is utilized in CECM to detect emotion topics' in event-related tweets, and then context-sensitive topical PageRank is utilized to detect meaningful multiword expressions as emotion causes. Experimental results on a dataset from Sina Weibo, one of the largest microblogging websites in China, show CECM can better detect emotion causes than baseline methods.

http://arxiv.org/abs/1504.08050v1

cs.CL, cs.AI, 68P20, H.2.8

Learning Contextualized Music Semantics from Tags via a Siamese Network

Ubai Sandouk, Ke Chen

Music information retrieval faces a challenge in modeling contextualized musical concepts formulated by a set of co-occurring tags. In this paper, we investigate the suitability of our recently proposed approach based on a Siamese neural network in fighting off this challenge. By means of tag features and probabilistic topic models, the network captures contextualized semantics from tags via unsupervised learning. This leads to a distributed semantics space and a potential solution to the out of vocabulary problem which has yet to be sufficiently addressed. We explore the nature of the resultant music-based semantics and address computational needs. We conduct experiments on three public music tag collections -namely, CAL500, MagTag5K and Million Song Dataset- and compare our approach to a number of state-of-the-art semantics learning approaches. Comparative results suggest that this approach outperforms previous approaches in terms of semantic priming and music tag completion.

http://arxiv.org/abs/1504.07968v2

cs.LG, I.2.6

Automated Analysis and Prediction of Job Interview Performance

Iftekhar Naim, M. Iftekhar Tanveer, Daniel Gildea, Mohammed, Hoque

We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing the videos of 138 interview sessions with 69 internship-seeking undergraduates at the Massachusetts Institute of Technology (MIT). Our automated analysis includes facial expressions (e.g., smiles, head gestures, facial tracking points), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, and pauses) of the interviewees. The ground truth labels are derived by taking a weighted average over the ratings of 9 independent judges. Our framework can automatically predict the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.75 or higher, and can quantify the relative importance of prosody, language, and facial expressions. By analyzing the relative feature weights learned by the regression models, our framework recommends to speak more fluently, use less filler words, speak as "we" (vs. "I"), use more unique words, and smile more. We also find that the students who were rated highly while answering the first interview question were also rated highly overall (i.e., first impression matters). Finally, our MIT Interview dataset will be made available to other researchers to further validate and expand our findings.

http://arxiv.org/abs/1504.03425v1

cs.HC, cs.AI, cs.CL

Learning Arbitrary Statistical Mixtures of Discrete Distributions

Jian Li, Yuval Rabani, Leonard J. Schulman, Chaitanya Swamy

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$ directly, but only indirectly and in very noisy fashion, by sampling from $[n]$ repeatedly, independently $K$ times from the distribution $p$. The problem is to infer $\vartheta$ to high accuracy in transportation (earthmover) distance. We give the first efficient algorithms for learning this mixture model without making any restricting assumptions on the structure of the distribution $\vartheta$. We bound the quality of the solution as a function of the size of the samples $K$ and the number of samples used. Our model and results have applications to a variety of unsupervised learning scenarios, including learning topic models and collaborative filtering.

http://arxiv.org/abs/1504.02526v1

cs.LG, cs.DS

Learning Mixed Membership Mallows Models from Pairwise Comparisons

Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

We propose a novel parameterized family of Mixed Membership Mallows Models (M4) to account for variability in pairwise comparisons generated by a heterogeneous population of noisy and inconsistent users. M4 models individual preferences as a user-specific probabilistic mixture of shared latent Mallows components. Our key algorithmic insight for estimation is to establish a statistical connection between M4 and topic models by viewing pairwise comparisons as words, and users as documents. This key insight leads us to explore Mallows components with a separable structure and leverage recent advances in separable topic discovery. While separability appears to be overly restrictive, we nevertheless show that it is an inevitable outcome of a relatively small number of latent Mallows components in a world of large number of items. We then develop an algorithm based on robust extreme-point identification of convex polygons to learn the reference rankings, and is provably consistent with polynomial sample complexity guarantees. We demonstrate that our new model is empirically competitive with the current state-of-the-art approaches in predicting real-world preferences.

http://arxiv.org/abs/1504.00757v1

cs.LG, stat.ML

Automated Document Indexing via Intelligent Hierarchical Clustering: A Novel Approach

Rajendra Kumar Roul, Shubham Rohan Asthana, Sanjay Kumar Sahay

With the rising quantity of textual data available in electronic format, the need to organize it become a highly challenging task. In the present paper, we explore a document organization framework that exploits an intelligent hierarchical clustering algorithm to generate an index over a set of documents. The framework has been designed to be scalable and accurate even with large corpora. The advantage of the proposed algorithm lies in the need for minimal inputs, with much of the hierarchy attributes being decided in an automated manner using statistical methods. The use of topic modeling in a pre-processing stage ensures robustness to a range of variations in the input data. For experimental work 20-Newsgroups dataset has been used. The F- measure of the proposed approach has been compared with the traditional K-Means and K-Medoids clustering algorithms. Test results demonstrate the applicability, efficiency and effectiveness of our proposed approach. After extensive experimentation, we conclude that the framework shows promise for further research and specialized commercial applications.

http://arxiv.org/abs/1504.00191v1

cs.IR

Nonparametric Relational Topic Models through Dependent Gamma Processes

Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo

Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known in advance, and this is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model in this paper. Instead of using fixed-dimensional probability distributions in its generative model, we use stochastic processes. Specifically, a gamma process is assigned to each document, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different gamma process from the global gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.

http://arxiv.org/abs/1503.08542v1

stat.ML, cs.CL, cs.IR, cs.LG

Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process

Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo

Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) model to resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determine the number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in order to capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma process that accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm with each conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets show the capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topics simultaneously.

http://arxiv.org/abs/1503.08535v1

stat.ML, cs.IR, cs.LG

On some provably correct cases of variational inference for topic models

Pranjal Awasthi, Andrej Risteski

Variational inference is a very efficient and popular heuristic used in various forms in the context of latent variable models. It's closely related to Expectation Maximization (EM), and is applied when exact EM is computationally infeasible. Despite being immensely popular, current theoretical understanding of the effectiveness of variaitonal inference based algorithms is very limited. In this work we provide the first analysis of instances where variational inference algorithms converge to the global optimum, in the setting of topic models. More specifically, we show that variational inference provably learns the optimal parameters of a topic model under natural assumptions on the topic-word matrix and the topic priors. The properties that the topic word matrix must satisfy in our setting are related to the topic expansion assumption introduced in (Anandkumar et al., 2013), as well as the anchor words assumption in (Arora et al., 2012c). The assumptions on the topic priors are related to the well known Dirichlet prior, introduced to the area of topic modeling by (Blei et al., 2003). It is well known that initialization plays a crucial role in how well variational based algorithms perform in practice. The initializations that we use are fairly natural. One of them is similar to what is currently used in LDA-c, the most popular implementation of variational inference for topic models. The other one is an overlapping clustering algorithm, inspired by a work by (Arora et al., 2014) on dictionary learning, which is very simple and efficient. While our primary goal is to provide insights into when variational inference might work in practice, the multiplicative, rather than the additive nature of the variational inference updates forces us to use fairly non-standard proof arguments, which we believe will be of general interest.

http://arxiv.org/abs/1503.06567v2

cs.LG, cs.DS, stat.ML

Visualization of Clandestine Labs from Seizure Reports: Thematic Mapping and Data Mining Research Directions

William Hsu, Mohammed Abduljabbar, Ryuichi Osuga, Max Lu, Wesam Elshamy

The problem of spatiotemporal event visualization based on reports entails subtasks ranging from named entity recognition to relationship extraction and mapping of events. We present an approach to event extraction that is driven by data mining and visualization goals, particularly thematic mapping and trend analysis. This paper focuses on bridging the information extraction and visualization tasks and investigates topic modeling approaches. We develop a static, finite topic model and examine the potential benefits and feasibility of extending this to dynamic topic modeling with a large number of topics and continuous time. We describe an experimental test bed for event mapping that uses this end-to-end information retrieval system, and report preliminary results on a geoinformatics problem: tracking of methamphetamine lab seizure events across time and space.

http://arxiv.org/abs/1503.01549v1

cs.IR, cs.CL

A Novel Approach for Clone Group Mapping by using Topic Modeling

Ruixia Zhang, Liping Zhang, Huan Wang, Zhuo Chen

Clone group mapping has a very important significance in the evolution of code clone. The topic modeling techniques were applied into code clone firstly and a new clone group mapping method was proposed. The method is very effective for not only Type-1 and Type-2 clone but also Type-3 clone .By making full use of the source text and structure information, topic modeling techniques transform the mapping problem of high-dimensional code space into a low-dimensional topic space, the goal of clone group mapping was indirectly reached by mapping clone group topics. Experiments on four open source software show that the recall and precision are up to 0.99, thus the method can effectively and accurately reach the goal of clone group mapping.

http://arxiv.org/abs/1502.03726v1

cs.SE

Ordering-sensitive and Semantic-aware Topic Modeling

Min Yang, Tianyi Cui, Wenting Tu

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the "bag-of-words" assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.

http://arxiv.org/abs/1502.03630v1

cs.LG, cs.CL, cs.IR

A Latent Variable Model Approach to PMI-based Word Embeddings

Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski

Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model of~\citet{mnih2007three}. The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by~\citet{mikolov2013efficient} and many subsequent papers. Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.

http://arxiv.org/abs/1502.03520v8

cs.LG, cs.CL, stat.ML

Dense v.s. Sparse: A Comparative Study of Sampling Analysis in Scene Classification of High-Resolution Remote Sensing Imagery

Jingwen Hu, Gui-Song Xia, Fan Hu, Liangpei Zhang

Scene classification is a key problem in the interpretation of high-resolution remote sensing imagery. Many state-of-the-art methods, e.g. bag-of-visual-words model and its variants, the topic models as well as deep learning-based approaches, share similar procedures: patch sampling, feature description/learning and classification. Patch sampling is the first and a key procedure which has a great influence on the results. In the literature, many different sampling strategies have been used, {e.g. dense sampling, random sampling, keypoint-based sampling and saliency-based sampling, etc. However, it is still not clear which sampling strategy is suitable for the scene classification of high-resolution remote sensing images. In this paper, we comparatively study the effects of different sampling strategies under the scenario of scene classification of high-resolution remote sensing images. We divide the existing sampling methods into two types: dense sampling and sparse sampling, the later of which includes random sampling, keypoint-based sampling and various saliency-based sampling proposed recently. In order to compare their performances, we rely on a standard bag-of-visual-words model to construct our testing scheme, owing to their simplicity, robustness and efficiency. The experimental results on two commonly used datasets show that dense sampling has the best performance among all the strategies but with high spatial and computational complexity, random sampling gives better or comparable results than other sparse sampling methods, like the sophisticated multi-scale key-point operators and the saliency-based methods which are intensively studied and commonly used recently.

http://arxiv.org/abs/1502.01097v2

cs.CV

User Clustering in Online Advertising via Topic Models

Sahin Cem Geyik, Ali Dasdan, Kuang-Chih Lee

In the domain of online advertising, our aim is to serve the best ad to a user who visits a certain webpage, to maximize the chance of a desired action to be performed by this user after seeing the ad. While it is possible to generate a different prediction model for each user to tell if he/she will act on a given ad, the prediction result typically will be quite unreliable with huge variance, since the desired actions are extremely sparse, and the set of users is huge (hundreds of millions) and extremely volatile, i.e., a lot of new users are introduced everyday, or are no longer valid. In this paper we aim to improve the accuracy in finding users who will perform the desired action, by assigning each user to a cluster, where the number of clusters is much smaller than the number of users (in the order of hundreds). Each user will fall into the same cluster with another user if their event history are similar. For this purpose, we modify the probabilistic latent semantic analysis (pLSA) model by assuming the independence of the user and the cluster id, given the history of events. This assumption helps us to identify a cluster of a new user without re-clustering all the users. We present the details of the algorithm we employed as well as the distributed implementation on Hadoop, and some initial results on the clusters that were generated by the algorithm.

http://arxiv.org/abs/1501.06595v2

cs.AI, cs.IR

Deep Belief Nets for Topic Modeling

Lars Maaloe, Morten Arngren, Ole Winther

Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets (DBN) that both find low-dimensional latent representations for documents. Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an online publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance evaluation of the DBN model and comparisons to the LDA model.

http://arxiv.org/abs/1501.04325v1

cs.CL, cs.LG, stat.ML

Bayesian Nonparametrics in Topic Modeling: A Brief Tutorial

Alexander Spangher

Using nonparametric methods has been increasingly explored in Bayesian hierarchical modeling as a way to increase model flexibility. Although the field shows a lot of promise, inference in many models, including Hierachical Dirichlet Processes (HDP), remain prohibitively slow. One promising path forward is to exploit the submodularity inherent in Indian Buffet Process (IBP) to derive near-optimal solutions in polynomial time. In this work, I will present a brief tutorial on Bayesian nonparametric methods, especially as they are applied to topic modeling. I will show a comparison between different non-parametric models and the current state-of-the-art parametric model, Latent Dirichlet Allocation (LDA).

http://arxiv.org/abs/1501.03861v1

stat.ML

Autodetection and Classification of Hidden Cultural City Districts from Yelp Reviews

Harini Suresh, Nicholas Locascio

Topic models are a way to discover underlying themes in an otherwise unstructured collection of documents. In this study, we specifically used the Latent Dirichlet Allocation (LDA) topic model on a dataset of Yelp reviews to classify restaurants based off of their reviews. Furthermore, we hypothesize that within a city, restaurants can be grouped into similar "clusters" based on both location and similarity. We used several different clustering methods, including K-means Clustering and a Probabilistic Mixture Model, in order to uncover and classify districts, both well-known and hidden (i.e. cultural areas like Chinatown or hearsay like "the best street for Italian restaurants") within a city. We use these models to display and label different clusters on a map. We also introduce a topic similarity heatmap that displays the similarity distribution in a city to a new restaurant.

http://arxiv.org/abs/1501.02527v1

cs.CL, cs.AI, cs.IR

Using Linear Dynamical Topic Model for Inferring Temporal Social Correlation in Latent Space

Freddy Chong Tat Chua, Richard J. Oentaryo, Ee-Peng Lim

The abundance of online user data has led to a surge of interests in understanding the dynamics of social relationships using computational methods. Utilizing users' items adoption data, we develop a new method to compute the Granger-causal (GC) relationships among users. In order to handle the high dimensional and sparse nature of the adoption data, we propose to model the relationships among users in latent space instead of the original data space. We devise a Linear Dynamical Topic Model (LDTM) that can capture the dynamics of the users' items adoption behaviors in latent (topic) space. Using the time series of temporal topic distributions learned by LDTM, we conduct Granger causality tests to measure the social correlation relationships between pairs of users. We call the combination of our LDTM and Granger causality tests as Temporal Social Correlation. By conducting extensive experiments on bibliographic data, where authors are analogous to users, we show that the ordering of authors' name on their publications plays a statistically significant role in the interaction of research topics among the authors. We also present a case study to illustrate the correlational relationships between pairs of authors.

http://arxiv.org/abs/1501.01270v1

cs.SI

Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts

Yuan Zuo, Jichang Zhao, Ke Xu

The short text has been the prevalent format for information of Internet in recent decades, especially with the development of online social media, whose millions of users generate a vast number of short messages everyday. Although sophisticated signals delivered by the short text make it a promising source for topic modeling, its extreme sparsity and imbalance brings unprecedented challenges to conventional topic models like LDA and its variants. Aiming at presenting a simple but general solution for topic modeling in short texts, we present a word co-occurrence network based model named WNTM to tackle the sparsity and imbalance simultaneously. Different from previous approaches, WNTM models the distribution over topics for each word instead of learning topics for each document, which successfully enhance the semantic density of data space without importing too much time or space complexity. Meanwhile, the rich contextual information preserved in the word-word space also guarantees its sensitivity in identifying rare topics with convincing quality. Furthermore, employing the same Gibbs sampling with LDA makes WNTM easily to be extended to various application scenarios. Extensive validations on both short and normal texts testify the outperformance of WNTM as compared to baseline methods. And finally we also demonstrate its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.

http://arxiv.org/abs/1412.5404v1

cs.CL, cs.IR

Application of Topic Models to Judgments from Public Procurement Domain

Michał Łopuszyński

In this work, automatic analysis of themes contained in a large corpora of judgments from public procurement domain is performed. The employed technique is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed, to use LDA in conjunction with recently developed method of unsupervised keyword extraction. Such an approach improves the interpretability of the automatically obtained topics and allows for better computational performance. The described analysis illustrates a potential of the method in detecting recurring themes and discovering temporal trends in lodged contract appeals. These results may be in future applied to improve information retrieval from repositories of legal texts or as auxiliary material for legal analyses carried out by human experts.

http://arxiv.org/abs/1412.5212v1

cs.CL

A Scalable Asynchronous Distributed Algorithm for Topic Modeling

Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N Vishwanathan, Inderjit S. Dhillon

Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands). Second, one needs a scalable and efficient way of distributing the computation across multiple machines. In this paper we present a novel algorithm F+Nomad LDA which simultaneously tackles both these problems. In order to handle large number of topics we use an appropriately modified Fenwick tree. This data structure allows us to sample from a multinomial distribution over $T$ items in $O(\log T)$ time. Moreover, when topic counts change the data structure can be updated in $O(\log T)$ time. In order to distribute the computation across multiple processor we present a novel asynchronous framework inspired by the Nomad algorithm of \cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform state-of-the-art on massive problems which involve millions of documents, billions of words, and thousands of topics.

http://arxiv.org/abs/1412.4986v1

cs.DC, cs.IR, cs.LG

A Topic Modeling Approach to Ranking

Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural way. We show how the estimation of latent rankings in the new generative model can be formally reduced to the estimation of topics in a statistically equivalent topic modeling problem. We leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable consistency as well as sample and computational complexity guarantees. We demonstrate that the new approach is empirically competitive with the current state-of-the-art approaches in predicting preferences on some semi-synthetic and real world datasets.

http://arxiv.org/abs/1412.3705v3

cs.LG, stat.ML

LightLDA: Big Topic Models on Modest Compute Clusters

Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric P. Xing, Tie-Yan Liu, Wei-Ying Ma

When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers. We consider this challenge in the context of topic modeling on web-scale corpora, and show that with a modest cluster of as few as 8 machines, we can train a topic model with 1 million topics and a 1-million-word vocabulary (for a total of 1 trillion parameters), on a document collection with 200 billion tokens -- a scale not yet reported even with thousands of machines. Our major contributions include: 1) a new, highly efficient O(1) Metropolis-Hastings sampling algorithm, whose running cost is (surprisingly) agnostic of model size, and empirically converges nearly an order of magnitude faster than current state-of-the-art Gibbs samplers; 2) a structure-aware model-parallel scheme, which leverages dependencies within the topic model, yielding a sampling strategy that is frugal on machine memory and network communication; 3) a differential data-structure for model storage, which uses separate data structures for high- and low-frequency words to allow extremely large models to fit in memory, while maintaining high inference speed; and 4) a bounded asynchronous data-parallel scheme, which allows efficient distributed processing of massive data via a parameter server. Our distribution strategy is an instance of the model-and-data-parallel programming model underlying the Petuum framework for general distributed ML, and was implemented on top of the Petuum open-source system. We provide experimental evidence showing how this development puts massive models within reach on a small cluster while still enjoying proportional time cost reductions with increasing cluster size, in comparison with alternative options.

http://arxiv.org/abs/1412.1576v1

stat.ML, cs.DC, cs.IR, cs.LG

Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic Models and Encyclopedic Knowledge

Vivi Nastase, Angela Fahrni

We present a method for coarse-grained cross-lingual alignment of comparable texts: segments consisting of contiguous paragraphs that discuss the same theme (e.g. history, economy) are aligned based on induced multilingual topics. The method combines three ideas: a two-level LDA model that filters out words that do not convey themes, an HMM that models the ordering of themes in the collection of documents, and language-independent concept annotations to serve as a cross-language bridge and to strengthen the connection between paragraphs in the same segment through concept relations. The method is evaluated on English and French data previously used for monolingual alignment. The results show state-of-the-art performance in both monolingual and cross-lingual settings.

http://arxiv.org/abs/1411.7820v1

cs.CL

Modeling Word Relatedness in Latent Dirichlet Allocation

Xun Wang

Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating infrequently occurring words or multi-language topic modeling. Experimental results demonstrate the effectiveness of our model compared with standard LDA.

http://arxiv.org/abs/1411.2328v1

cs.CL, cs.AI

Model-Parallel Inference for Big Topic Models

Xun Zheng, Jin Kyu Kim, Qirong Ho, Eric P. Xing

In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so-called "big model", is becoming the next desideratum after enthusiasms on "big data", especially for fine-grained downstream tasks such as online advertising, where good performances are usually achieved by regression-based predictors built on millions if not billions of input features. The conventional data-parallel approach for training gigantic topic models turns out to be rather inefficient in utilizing the power of parallelism, due to the heavy dependency on a centralized image of "model". Big model size also poses another challenge on the storage, where available model size is bounded by the smallest RAM of nodes. To address these issues, we explore another type of parallelism, namely model-parallelism, which enables training of disjoint blocks of a big topic model in parallel. By integrating data-parallelism with model-parallelism, we show that dependencies between distributed elements can be handled seamlessly, achieving not only faster convergence but also an ability to tackle significantly bigger model size. We describe an architecture for model-parallel inference of LDA, and present a variant of collapsed Gibbs sampling algorithm tailored for it. Experimental results demonstrate the ability of this system to handle topic modeling with unprecedented amount of 200 billion model variables only on a low-end cluster with very limited computational resources and bandwidth.

http://arxiv.org/abs/1411.2305v1

cs.DC, cs.LG, stat.ML

Eigenvectors of Orthogonally Decomposable Functions

Mikhail Belkin, Luis Rademacher, James Voss

The Eigendecomposition of quadratic forms (symmetric matrices) guaranteed by the spectral theorem is a foundational result in applied mathematics. Motivated by a shared structure found in inferential problems of recent interest---namely orthogonal tensor decompositions, Independent Component Analysis (ICA), topic models, spectral clustering, and Gaussian mixture learning---we generalize the eigendecomposition from quadratic forms to a broad class of "orthogonally decomposable" functions. We identify a key role of convexity in our extension, and we generalize two traditional characterizations of eigenvectors: First, the eigenvectors of a quadratic form arise from the optima structure of the quadratic form on the sphere. Second, the eigenvectors are the fixed points of the power iteration. In our setting, we consider a simple first order generalization of the power method which we call gradient iteration. It leads to efficient and easily implementable methods for basis recovery. It includes influential Machine Learning methods such as cumulant-based FastICA and the tensor power iteration for orthogonally decomposable tensors as special cases. We provide a complete theoretical analysis of gradient iteration using the structure theory of discrete dynamical systems to show almost sure convergence and fast (super-linear) convergence rates. The analysis also extends to the case when the observed function is only approximately orthogonally decomposable, with bounds that are polynomial in dimension and other relevant parameters, such as perturbation size. Our perturbation results can be considered as a non-linear version of the classical Davis-Kahan theorem for perturbations of eigenvectors of symmetric matrices.

http://arxiv.org/abs/1411.1420v6

cs.LG

Population Empirical Bayes

Alp Kucukelbir, David M. Blei

Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does not match the data, predictive accuracy suffers. We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis. We introduce a new concept, the latent dataset, as a hierarchical variable and set the empirical population as its prior. This leads to a new predictive density that mitigates model mismatch. We efficiently apply this method to complex models by proposing a stochastic variational inference algorithm, called bumping variational inference (BUMP-VI). We demonstrate improved predictive accuracy over classical Bayesian inference in three models: a linear regression model of health data, a Bayesian mixture model of natural images, and a latent Dirichlet allocation topic model of scientific documents.

http://arxiv.org/abs/1411.0292v2

stat.ML, cs.LG

Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling

Mingyuan Zhou

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference for the BNBP has to truncate the number of atoms of the beta process. This paper introduces an exchangeable partition probability function to explicitly describe how the BNBP clusters the data points of each group into a random number of exchangeable partitions, which are shared across all the groups. A fully collapsed Gibbs sampler is developed for the BNBP, leading to a novel nonparametric Bayesian topic model that is distinct from existing ones, with simple implementation, fast convergence, good mixing, and state-of-the-art predictive performance.

http://arxiv.org/abs/1410.7812v2

stat.ME, stat.ML

A provable SVD-based algorithm for learning topics in dominant admixture corpus

Trapit Bansal, Chiranjib Bhattacharyya, Ravindran Kannan

Topic models, such as Latent Dirichlet Allocation (LDA), posit that documents are drawn from admixtures of distributions over words, known as topics. The inference problem of recovering topics from admixtures, is NP-hard. Assuming separability, a strong assumption, [4] gave the first provable algorithm for inference. For LDA model, [6] gave a provable algorithm using tensor-methods. But [4,6] do not learn topic vectors with bounded $l_1$ error (a natural measure for probability vectors). Our aim is to develop a model which makes intuitive and empirically supported assumptions and to design an algorithm with natural, simple components such as SVD, which provably solves the inference problem for the model with bounded $l_1$ error. A topic in LDA and other models is essentially characterized by a group of co-occurring words. Motivated by this, we introduce topic specific Catchwords, group of words which occur with strictly greater frequency in a topic than any other topic individually and are required to have high frequency together rather than individually. A major contribution of the paper is to show that under this more realistic assumption, which is empirically verified on real corpora, a singular value decomposition (SVD) based algorithm with a crucial pre-processing step of thresholding, can provably recover the topics from a collection of documents drawn from Dominant admixtures. Dominant admixtures are convex combination of distributions in which one distribution has a significantly higher contribution than others. Apart from the simplicity of the algorithm, the sample complexity has near optimal dependence on $w_0$, the lowest probability that a topic is dominant, and is better than [4]. Empirical evidence shows that on several real world corpora, both Catchwords and Dominant admixture assumptions hold and the proposed algorithm substantially outperforms the state of the art [5].

http://arxiv.org/abs/1410.6991v3

stat.ML, cs.LG

Model Selection for Topic Models via Spectral Decomposition

Dehua Cheng, Xinran He, Yan Liu

Topic models have achieved significant successes in analyzing large-scale text corpus. In practical applications, we are always confronted with the challenge of model selection, i.e., how to appropriately set the number of topics. Following recent advances in topic model inference via tensor decomposition, we make a first attempt to provide theoretical analysis on model selection in latent Dirichlet allocation. Under mild conditions, we derive the upper bound and lower bound on the number of topics given a text collection of finite size. Experimental results demonstrate that our bounds are accurate and tight. Furthermore, using Gaussian mixture model as an example, we show that our methodology can be easily generalized to model selection analysis for other latent models.

http://arxiv.org/abs/1410.6466v2

stat.ML, cs.IR, cs.LG, stat.CO, 62H30, H.3.3

Graph-Sparse LDA: A Topic Model with Structured Sparsity

Finale Doshi-Velez, Byron Wallace, Ryan Adams

Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the model vary depending on the application: in some cases, the discovered topics may be used for prediction or some other downstream task. In other cases, the content of the topic itself may be of intrinsic scientific interest. Unfortunately, even using modern sparse techniques, the discovered topics are often difficult to interpret due to the high dimensionality of the underlying space. To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that leverages knowledge of relationships between words (e.g., as encoded by an ontology). In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words. Graph-Sparse LDA recovers sparse, interpretable summaries on two real-world biomedical datasets while matching state-of-the-art prediction performance.

http://arxiv.org/abs/1410.4510v2

stat.ML, cs.CL, cs.LG

Zero-Shot Object Recognition System based on Topic Model

Wai Lam Hoo, Chee Seng Chan

Object recognition systems usually require fully complete manually labeled training data to train the classifier. In this paper, we study the problem of object recognition where the training samples are missing during the classifier learning stage, a task also known as zero-shot learning. We propose a novel zero-shot learning strategy that utilizes the topic model and hierarchical class concept. Our proposed method advanced where cumbersome human annotation stage (i.e. attribute-based classification) is eliminated. We achieve comparable performance with state-of-the-art algorithms in four public datasets: PubFig (67.09%), Cifar-100 (54.85%), Caltech-256 (52.14%), and Animals with Attributes (49.65%) when unseen classes exist in the classification task.

http://arxiv.org/abs/1410.3748v1

cs.CV, stat.ML

Annotation as a New Paradigm in Research Archiving

Dirk Roorda, Charles van den Heuvel

We outline a paradigm to preserve results of digital scholarship, whether they are query results, feature values, or topic assignments. This paradigm is characterized by using annotations as multifunctional carriers and making them portable. The testing grounds we have chosen are two significant enterprises, one in the history of science, and one in Hebrew scholarship. The first one (CKCC) focuses on the results of a project where a Dutch consortium of universities, research institutes, and cultural heritage institutions experimented for 4 years with language techniques and topic modeling methods with the aim to analyze the emergence of scholarly debates. The data: a complex set of about 20.000 letters. The second one (DTHB) is a multi-year effort to express the linguistic features of the Hebrew bible in a text database, which is still growing in detail and sophistication. Versions of this database are packaged in commercial bible study software. We state that the results of these forms of scholarship require new knowledge management and archive practices. Only when researchers can build efficiently on each other's (intermediate) results, they can achieve the aggregations of quality data by which new questions can be answered, and hidden patterns visualized. Archives are required to find a balance between preserving authoritative versions of sources and supporting collaborative efforts in digital scholarship. Annotations are promising vehicles for preserving and reusing research results. Keywords annotation, portability, archiving, queries, features, topics, keywords, Republic of Letters, Hebrew text databases.

http://arxiv.org/abs/1412.6069v1

cs.DL, cs.CL

Probit Normal Correlated Topic Models

Xingchen Yu, Ernest Fokoue

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaus- sian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many authors have so far con- centrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial probit estimation by using an adaptation of the diagonal orthant multinomial probit in the topic models context, resulting in the ability of our topic modelling scheme to handle corpuses with a large number of latent topics. An additional and very important benefit of our method lies in the fact that unlike with the logistic normal model whose non-conjugacy leads to the need for sophisticated sampling schemes, our ap- proach exploits the natural conjugacy inherent in the auxiliary formulation of the probit model to achieve greater simplicity. The application of our proposed scheme to a well known Associated Press corpus not only helps discover a large number of meaningful topics but also reveals the capturing of compellingly intuitive correlations among certain topics. Besides, our proposed approach lends itself to even further scalability thanks to various existing high performance algorithms and architectures capable of handling millions of documents.

http://arxiv.org/abs/1410.0908v1

stat.ML, cs.IR, cs.LG, 62H25, 62H30