Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting. Existing methods such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) cannot solve this prob- lem very well since only very limited word co-occurrence information is available in short texts. This paper studies how to incorporate the external word correlation knowledge into short texts to improve the coherence of topic modeling. Based on recent results in word embeddings that learn se- mantically representations for words from a large corpus, we introduce a novel method, Embedding-based Topic Model (ETM), to learn latent topics from short texts. ETM not only solves the problem of very limited word co-occurrence information by aggregating short texts into long pseudo- texts, but also utilizes a Markov Random Field regularized model that gives correlated words a better chance to be put into the same topic. The experiments on real-world datasets validate the effectiveness of our model comparing with the state-of-the-art models.
The earliest works on short text topic models mainly focused on exploiting external knowledge to enrich the representation of short texts. For instance, @cite_25 first found the related long texts for each short text, and learned topics over short texts and their related long texts using LDA. @cite_0 learned the topics on another large-scale dataset using a conventional topic model such as PLSA and LDA for short text classification. However, these models are only effective when the additional data are closely related to the original data. Furthermore, finding such additional data may be expensive or even impossible.
@cite_0 @cite_25
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In this work, we address the problem of joint modeling of text and citations in the topic modeling framework. We present two different models called the Pairwise-Link-LDA and the Link-PLSA-LDA models. The Pairwise-Link-LDA model combines the ideas of LDA [4] and Mixed Membership Block Stochastic Models [1] and allows modeling arbitrary link structure. However, the model is computationally expensive, since it involves modeling the presence or absence of a citation (link) between every pair of documents. The second model solves this problem by assuming that the link structure is a bipartite graph. As the name indicates, Link-PLSA-LDA model combines the LDA and PLSA models into a single graphical model. Our experiments on a subset of Citeseer data show that both these models are able to predict unseen data better than the baseline model of Erosheva and Lafferty [8], by capturing the notion of topical similarity between the contents of the cited and citing documents. Our experiments on two different data sets on the link prediction task show that the Link-PLSA-LDA model performs the best on the citation prediction task, while also remaining highly scalable. In addition, we also present some interesting visualizations generated by each of the models. Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model. Understanding the rapidly growing short text is very important. Short text is different from traditional documents in its shortness and sparsity, which hinders the application of conventional machine learning and text mining algorithms. Two major approaches have been exploited to enrich the representation of short text. One is to fetch contextual information of a short text to directly add more text; the other is to derive latent topics from existing large corpus, which are used as features to enrich the representation of short text. The latter approach is elegant and efficient in most cases. The major trend along this direction is to derive latent topics of certain granularity through well-known topic models such as latent Dirichlet allocation (LDA). However, topics of certain granularity are usually not sufficient to set up effective feature spaces. In this paper, we move forward along this direction by proposing an method to leverage topics at multiple granularity, which can model the short text more precisely. Taking short text classification as an example, we compared our proposed method with the state-of-the-art baseline over one open data set. Our method reduced the classification error by 20.25 and 16.68 respectively on two classifiers. This paper presents online topic model (OLDA), a topic model that automatically captures the thematic patterns and identifies emerging topics of text streams and their changes over time. Our approach allows the topic modeling framework, specifically the latent Dirichlet allocation (LDA) model, to work in an online fashion such that it incrementally builds an up-to-date model (mixture of topics per document and mixture of words per topic) when a new document (or a set of documents) appears. A solution based on the empirical Bayes method is proposed. The idea is to incrementally update the current model according to the information inferred from the new stream of data with no need to access previous data. The dynamics of the proposed approach also provide an efficient mean to track the topics over time and detect the emerging topics in real time. Our method is evaluated both qualitatively and quantitatively using benchmark datasets. In our experiments, the OLDA has discovered interesting patterns by just analyzing a fraction of data at a time. Our tests also prove the ability of OLDA to align the topics across the epochs with which the evolution of the topics over time is captured. The OLDA is also comparable to, and sometimes better than, the original LDA in predicting the likelihood of unseen documents. Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation even though, for example, a document about genetics is more likely to also be about disease than X-ray astronomy. This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions. In this paper we develop the correlated topic model (CTM), where the topic proportions exhibit correlation via the logistic normal distribution [J. Roy. Statist. Soc. Ser. B 44 (1982) 139--177]. We derive a fast variational inference algorithm for approximate posterior inference in this model, which is complicated by the fact that the logistic normal is not conjugate to the multinomial. We apply the CTM to the articles from Science published from 1990--1999, a data set that comprises 57M words. The CTM gives a better fit of the data than LDA, and we demonstrate its use as an exploratory tool of large document collections. We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis VAST paper data set and product review data sets. 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 @math 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 @math 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 @math , 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]. Topic Model such as Latent Dirichlet Allocation(LDA) makes assumption that topic assignment of different words are conditionally independent. In this paper, we propose a new model Extended Global Topic Random Field (EGTRF) to model non-linear dependencies between words. Specifically, we parse sentences into dependency trees and represent them as a graph, and assume the topic assignment of a word is influenced by its adjacent words and distance-2 words. Word similarity information learned from large corpus is incorporated to enhance word topic assignment. Parameters are estimated efficiently by variational inference and experimental results on two datasets show EGTRF achieves lower perplexity and higher log predictive probability. The overwhelming amount of short text data on social media and elsewhere has posed great challenges to topic modeling due to the sparsity problem. Most existing attempts to alleviate this problem resort to heuristic strategies to aggregate short texts into pseudo-documents before the application of standard topic modeling. Although such strategies cannot be well generalized to more general genres of short texts, the success has shed light on how to develop a generalized solution. In this paper, we present a novel model towards this goal by integrating topic modeling with short text aggregation during topic inference. The aggregation is founded on general topical affinity of texts rather than particular heuristics, making the model readily applicable to various short texts. Experimental results on real-world datasets validate the effectiveness of this new model, suggesting that it can distill more meaningful topics from short texts. Given a set of images containing multiple object categories, we seek to discover those categories and their image locations without supervision. We achieve this using generative models from the statistical text literature: probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet Allocation (LDA). In text analysis these are used to discover topics in a corpus using the bag-of-words document representation. Here we discover topics as object categories, so that an image containing instances of several categories is modelled as a mixture of topics. The models are applied to images by using a visual analogue of a word, formed by vector quantizing SIFT like region descriptors. We investigate a set of increasingly demanding scenarios, starting with image sets containing only two object categories through to sets containing multiple categories (including airplanes, cars, faces, motorbikes, spotted cats) and background clutter. The object categories sample both intra-class and scale variation, and both the categories and their approximate spatial layout are found without supervision. We also demonstrate classification of unseen images and images containing multiple objects. Performance of the proposed unsupervised method is compared to the semi-supervised approach of [7].1 1This work was sponsored in part by the EU Project CogViSys, the University of Oxford, Shell Oil, and the National Geospatial-Intelligence Agency. With the rapid growth of social Web applications such as Twitter and online advertisements, the task of understanding short texts is becoming more and more important. Most traditional text mining techniques are designed to handle long text documents. For short text messages, many of the existing techniques are not effective due to the sparseness of text representations. To understand short messages, we observe that it is often possible to find topically related long texts, which can be utilized as the auxiliary data when mining the target short texts data. In this article, we present a novel approach to cluster short text messages via transfer learning from auxiliary long text data. We show that while some previous work exists that enhance short text clustering with related long texts, most of them ignore the semantic and topical inconsistencies between the target and auxiliary data and hurt the clustering performance. To accommodate the possible inconsistency between source and target data, we propose a novel topic model - Dual Latent Dirichlet Allocation (DLDA) model, which jointly learns two sets of topics on short and long texts and couples the topic parameters to cope with the potential inconsistency between data sets. We demonstrate through large-scale clustering experiments on both advertisements and Twitter data that we can obtain superior performance over several state-of-art techniques for clustering short text documents. Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines. Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring hidden topics in text corpora. Standard LDA model suffers the problem that the topic assignment of each word is independent and lacks the mechanism to utilize the rich prior background knowledge to learn semantically coherent topics. To address this problem, in this paper, we propose a model called Entity Correlation Latent Dirichlet Allocation (EC-LDA) by incorporating constraints derived from entity correlations as the prior knowledge into LDA topic model. Different from other knowledge-based topic models which extract the knowledge information directly from the train dataset itself or even from the human judgements, for our work, we take advantage of the prior knowledge from the external knowledge base (Freebase 1, in our experiment). Hence, our approach is more suitable to widely kinds of text corpora in different scenarios. We fit our proposed model using Gibbs sampling. Experiment results demonstrate the effectiveness of our model compared with standard LDA. Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new documents without retraining the model. In this paper, we empirically evaluate the performance of several methods for topic inference in previously unseen documents, including methods based on Gibbs sampling, variational inference, and a new method inspired by text classification. The classification-based inference method produces results similar to iterative inference methods, but requires only a single matrix multiplication. In addition to these inference methods, we present SparseLDA, an algorithm and data structure for evaluating Gibbs sampling distributions. Empirical results indicate that SparseLDA can be approximately 20 times faster than traditional LDA and provide twice the speedup of previously published fast sampling methods, while also using substantially less memory. Algorithms such as Latent Dirichlet Allocation (LDA) have achieved significant progress in modeling word document relationships. These algorithms assume each word in the document was generated by a hidden topic and explicitly model the word distribution of each topic as well as the prior distribution over topics in the document. Given these parameters, the topics of all words in the same document are assumed to be independent. In this paper, we propose modeling the topics of words in the document as a Markov chain. Specifically, we assume that all words in the same sentence have the same topic, and successive sentences are more likely to have the same topics. Since the topics are hidden, this leads to using the well-known tools of Hidden Markov Models for learning and inference. We show that incorporating this dependency allows us to learn better topics and to disambiguate words that can belong to different topics. Quantitatively, we show that we obtain better perplexity in modeling documents with only a modest increase in learning and inference complexity. Probabilistic latent topic models have recently enjoyed much success in extracting and analyzing latent topics in text in an unsupervised way. One common deficiency of existing topic models, though, is that they would not work well for extracting cross-lingual latent topics simply because words in different languages generally do not co-occur with each other. In this paper, we propose a way to incorporate a bilingual dictionary into a probabilistic topic model so that we can apply topic models to extract shared latent topics in text data of different languages. Specifically, we propose a new topic model called Probabilistic Cross-Lingual Latent Semantic Analysis (PCLSA) which extends the Probabilistic Latent Semantic Analysis (PLSA) model by regularizing its likelihood function with soft constraints defined based on a bilingual dictionary. Both qualitative and quantitative experimental results show that the PCLSA model can effectively extract cross-lingual latent topics from multilingual text data. Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency. Latent Dirichlet Allocation (LDA) mining thematic structure of documents plays an important role in nature language processing and machine learning areas. However, the probability distribution from LDA only describes the statistical relationship of occurrences in the corpus and usually in practice, probability is not the best choice for feature representations. Recently, embedding methods have been proposed to represent words and documents by learning essential concepts and representations, such as Word2Vec and Doc2Vec. The embedded representations have shown more effectiveness than LDA-style representations in many tasks. In this paper, we propose the Topic2Vec approach which can learn topic representations in the same semantic vector space with words, as an alternative to probability. The experimental results show that Topic2Vec achieves interesting and meaningful results. Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.