Free Essay

Student

In: Other Topics

Submitted By KstrongStudios
Words 6498
Pages 26
topicmodels: An R Package for Fitting Topic Models
Bettina Grun ¨
Johannes Kepler Universit¨t Linz a

Kurt Hornik
WU Wirtschaftsuniversit¨t Wien a

Abstract This article is a (slightly) modified and shortened version of Gr¨n and Hornik (2011), u published in the Journal of Statistical Software. Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Keywords: Gibbs sampling, R, text analysis, topic model, variational EM.

1. Introduction
In machine learning and natural language processing topic models are generative models which provide a probabilistic framework for the term frequency occurrences in documents in a given corpus. Using only the term frequencies assumes that the information in which order the words occur in a document is negligible. This assumption is also referred to as the exchangeability assumption for the words in a document and this assumption leads to bag-of-words models. Topic models extend and build on classical methods in natural language processing such as the unigram model and the mixture of unigram models (Nigam, McCallum, Thrun, and Mitchell 2000) as well as Latent Semantic Analysis (LSA; Deerwester, Dumais, Furnas, Landauer, and Harshman 1990). Topic models differ from the unigram or the mixture of unigram models because they are mixed-membership models (see for example Airoldi, Blei, Fienberg, and Xing 2008). In the unigram model each word is assumed to be drawn from the same term distribution, in the mixture of unigram models a topic is drawn for each document and all words in a document are drawn from the term distribution of the topic. In mixed-membership models documents are not assumed to belong to single topics, but to simultaneously belong to several topics and the topic distributions vary over documents. An early topic model was proposed by Hofmann (1999) who developed probabilistic LSA. He assumed that the interdependence between words in a document can be explained by the latent topics the document belongs to. Conditional on the topic assignments of the words the word occurrences in a document are independent. The latent Dirichlet allocation (LDA; Blei, Ng, and Jordan 2003b) model is a Bayesian mixture model for discrete data where topics are

2

topicmodels: An R Package for Fitting Topic Models

assumed to be uncorrelated. The correlated topics model (CTM; Blei and Lafferty 2007) is an extension of the LDA model where correlations between topics are allowed. An introduction to topic models is given in Steyvers and Griffiths (2007) and Blei and Lafferty (2009). Topic models have previously been used for a variety of applications, including ad-hoc information retrieval (Wei and Croft 2006), geographical information retrieval (Li, Wang, Xie, Wang, and Ma 2008) and the analysis of the development of ideas over time in the field of computational linguistics (Hall, Jurafsky, and Manning 2008). C code for fitting the LDA model (http://www.cs.princeton.edu/~blei/lda-c/) and the CTM (http://www.cs.princeton.edu/~blei/ctm-c/) is available under the GPL from David M. Blei and co-authors, who introduced these models in their papers. The method used for fitting the models is the variational expectation-maximization (VEM) algorithm. Other implementations for fitting topic models—especially of the LDA model—are available. The standalone program lda (Mochihashi 2004a,b) provides standard VEM estimation. An implementation in Python of an online version of LDA using VEM estimation as described in Hoffman, Blei, and Bach (2010) is available under the GPL from the first author’s web page (http://www.cs.princeton.edu/~mdhoffma/). For Bayesian estimation using Gibbs sampling several implementations are available. GibbsLDA++ (Phan, Nguyen, and Horiguchi 2008) is available under the GPL from http://gibbslda.sourceforge.net/. The Matlab Topic Modeling Toolbox 1.3.2 (Griffiths and Steyvers 2004; Steyvers and Griffiths 2011) is free for scientific use. A license must be obtained from the authors to use it for commercial purposes. MALLET (McCallum 2002) is released under the CPL and is a Java-based package which is more general in allowing for statistical natural language processing, document classification, clustering, topic modeling using LDA, information extraction, and other machine learning applications to text. A general toolkit for implementing hierarchical Bayesian models is provided by the Hierarchical Bayes Compiler HBC (Daum´ III 2008), which also allows to e fit the LDA model. Another general framework for running Bayesian inference in graphical models which allows to fit the LDA model is available through Infer.NET (Microsoft Corporation 2010) The fast collapsed Gibbs sampling method is described in Porteous, Asuncion, Newman, Ihler, Smyth, and Welling (2008) and code is also available from the first author’s web page (http://www.ics.uci.edu/~iporteou/fastlda/). For R, an environment for statistical computing and graphics (R Development Core Team 2011), CRAN (http://CRAN.R-project.org) features two packages for fitting topic models: topicmodels and lda. The R package lda (Chang 2010) provides collapsed Gibbs sampling methods for LDA and related topic model variants, with the Gibbs sampler implemented in C. All models in package lda are fitted using Gibbs sampling for determining the posterior probability of the latent variables. Wrappers for the expectation-maximization (EM) algorithm are provided which build on this functionality for the E-step. Note that this implementation therefore differs in general from the estimation technique proposed in the original papers introducing these model variants, where the VEM algorithm is usually applied. The R package topicmodels currently provides an interface to the code for fitting an LDA model and a CTM with the VEM algorithm as implemented by Blei and co-authors and to the code for fitting an LDA topic model with Gibbs sampling written by Phan and co-authors. Package topicmodels builds on package tm (Feinerer, Hornik, and Meyer 2008; Feinerer 2011) which constitutes a framework for text mining applications within R. tm provides infrastructure for constructing a corpus, e.g., by reading in text data from PDF files, and transforming a corpus to a document-term matrix which is the input data for topic models. In package

Bettina Gr¨n, Kurt Hornik u

3

topicmodels the respective code is directly called through an interface at the C level avoiding file input and output, and hence substantially improving performance. The functionality for data input and output in the original code was substituted and R objects are directly used as input and S4 objects as output to R. The same main function allows fitting the LDA model with different estimation methods returning objects only slightly different in structure. In addition the strategies for model selection and inference are applicable in both cases. This allows for easy use and comparison of both current state-of-the-art estimation techniques for topic models. Packages topicmodels aims at extensibility by providing an interface for inclusion of other estimation methods of topic models. This paper is structured as follows: Section 2 introduces the specification of topic models, outlines the estimation with the VEM as well as Gibbs sampling and gives an overview of pre-processing steps and methods for model selection and inference. The main fitter functions in the package and the helper functions for analyzing a fitted model are presented in Section 3. An illustrative example for using the package is given in Section 4 where topic models are fitted to the corpus of abstracts in the Journal of Statistical Software.

2. Topic model specification and estimation
2.1. Model specification
For both models—LDA and CTM—the number of topics k has to be fixed a-priori. The LDA model and the CTM assume the following generative process for a document w = (w1 , . . . , wN ) of a corpus D containing N words from a vocabulary consisting of V different terms, wi ∈ {1, . . . , V } for all i = 1, . . . , N . For LDA the generative model consists of the following three steps. Step 1: The term distribution β is determined for each topic by β ∼ Dirichlet(δ). Step 2: The proportions θ of the topic distribution for the document w are determined by θ ∼ Dirichlet(α). Step 3: For each of the N words wi (a) Choose a topic zi ∼ Multinomial(θ). (b) Choose a word wi from a multinomial probability distribution conditioned on the topic zi : p(wi |zi , β). β is the term distribution of topics and contains the probability of a word occurring in a given topic. For CTM Step 2 is modified to

4

topicmodels: An R Package for Fitting Topic Models

Step 2a: The proportions θ of the topic distribution for the document w are determined by drawing η ∼ N (µ, Σ) with η ∈ R(k−1) and Σ ∈ R(k−1)×(k−1) . Set η = (η , 0). θ is given by ˜ θK = for K = 1, . . . , k. exp{˜K } η k η i=1 exp{˜i }

2.2. Estimation
For maximum likelihood (ML) estimation of the LDA model the log-likelihood of the data, i.e., the sum over the log-likelihoods of all documents, is maximized with respect to the model parameters α and β. In this setting β and not δ is in general the parameter of interest. For the CTM model the log-likelihood of the data is maximized with respect to the model parameters µ, Σ and β. For VEM estimation the log-likelihood for one document w ∈ D is for LDA given by (α, β) = log (p(w|α, β))
N

= log z i=1

p(wi |zi , β)p(zi |θ)

p(θ|α)dθ

and for CTM by (µ, Σ, β) = log (p(w|µ, Σ, β))
N

= log z i=1

p(wi |zi , β)p(zi |θ)

p(θ|µ, Σ)dθ.

The sum over z = (zi )i=1,...,N includes all combinations of assigning the N words in the document to the k topics. The quantities p(w|α, β) for the LDA model and p(w|µ, Σ, β) for the CTM cannot be tractably computed. Hence, a VEM procedure is used for estimation. The EM algorithm (Dempster, Laird, and Rubin 1977) is an iterative method for determining an ML estimate in a missing data framework where the complete likelihood of the observed and missing data is easier to maximize than the likelihood of the observed data only. It iterates between an Expectation (E)-step where the expected complete likelihood given the data and current parameter estimates is determined and a Maximization (M)-step where the expected complete likelihood is maximized to find new parameter estimates. For topic models the missing data in the EM algorithm are the latent variables θ and z for LDA and η and z for CTM. For topic models a VEM algorithm is used instead of an ordinary EM algorithm because the expected complete likelihood in the E-step is still computationally intractable. For an introduction into variational inference see for example Wainwright and Jordan (2008). To facilitate

Bettina Gr¨n, Kurt Hornik u

5

the E-step the posterior distribution p(θ, z|w, α, β) is replaced by a variational distribution q(θ, z|γ, φ). This implies that in the E-step instead of Ep [log p(θ, z|w, α, β)] the following is determined Eq [log p(θ, z|w, α, β)]. The parameters for the variational distributions are document specific and hence are allowed to vary over documents which is not the case for α and β. For the LDA model the variational parameters γ and φ for a given document w are determined by (γ ∗ , φ∗ ) = arg min DKL (q(θ, z|γ, φ)||p(θ, z|w, α, β)).
(γ,φ)

DKL denotes the Kullback-Leibler (KL) divergence. The variational distribution is set equal to
N

q(θ, z|γ, φ) = q1 (θ|γ) i=1 q2 (zi |φi ),

where q1 () is a Dirichlet distribution with parameters γ and q2 () is a multinomial distribution with parameters φi . Analogously for the CTM the variational parameters are determined by (λ∗ , ν ∗ , φ∗ ) = arg min DKL (q(η, z|λ, ν 2 , φ)||p(η, z|w, µ, Σ, β)).
(λ,ν,φ)

Since the variational parameters are fitted separately for each document the variational covariance matrix can be assumed to be diagonal. The variational distribution is set to k−1 N 2 q1 (ηK |λK , νK ) K=1 i=1

q(η, z|λ, ν , φ) =

2

q2 (zi |φi ),

2 where q1 () is a univariate Gaussian distribution with mean λK and variance νK , and q2 () again denotes a multinomial distribution with parameters φi . Using this simple model for η has the advantage that it is computationally less demanding while still providing enough flexibility. Over all documents this leads to a mixture of normal distributions with diagonal variance-covariance matrices. This mixture distribution allows to approximate the marginal distribution over all documents which has an arbitrary variance-covariance matrix.

For the LDA model it can be shown with the following equality that the variational parameters result in a lower bound for the log-likelihood log p(w|α, β) = L(γ, φ; α, β) + DKL (q(θ, z|γ, φ)||p(θ, z|w, α, β)) where L(γ, φ; α, β) = Eq [log p(θ, z, w|α, β)] − Eq [log q(θ, z)] (see Blei et al. 2003b, p. 1019). Maximizing the lower bound L(γ, φ; α, β) with respect to γ and φ is equivalent to minimizing the KL divergence between the variational posterior probability and the true posterior probability. This holds analogously for the CTM. For estimation the following steps are repeated until convergence of the lower bound of the log-likelihood.

6

topicmodels: An R Package for Fitting Topic Models

E-step: For each document find the optimal values of the variational parameters {γ, φ} for the LDA model and {λ, ν, φ} for the CTM. M-step: Maximize the resulting lower bound on the log-likelihood with respect to the model parameters α and β for the LDA model and µ, Σ and β for the CTM. For inference the latent variables θ and z are often of interest to determine which topics a document consists of and which topic a certain word in a document was drawn from. Under the assumption that the variational posterior probability is a good approximation of the true posterior probability it can be used to determine estimates for the latent variables. In the following inference is always based on the variational posterior probabilities if the VEM is used for estimation. For Gibbs sampling in the LDA model draws from the posterior distribution p(z|w) are obtained by sampling from p(zi = K|w, z−i ) ∝ i n−i,K + δ n−i,K + α i n−i,K + V δ n−i,. + kα

(j)

(d )

(.)

(d )

(see Griffiths and Steyvers 2004; Phan et al. 2008). z−i is the vector of current topic memberships of all words without the ith word wi . The index j indicates that wi is equal to (j) the jth term in the vocabulary. n−i,K gives how often the jth term of the vocabulary is currently assigned to topic K without the ith word. The dot . implies that summation over this index is performed. di indicates the document in the corpus to which word wi belongs. In the Bayesian model formulation δ and α are the parameters of the prior distributions for the term distribution of the topics β and the topic distribution of documents θ, respectively. The predictive distributions of the parameters θ and β given w and z are given by nK + δ ˆ(j) βK = (.) , nK + V δ for j = 1, . . . , V and d = 1, . . . , D.
(j)

nK + α ˆ(d) θK = (.) , nK + kα

(d)

2.3. Pre-processing
The input data for topic models is a document-term matrix. The rows in this matrix correspond to the documents and the columns to the terms. The entry mij indicates how often the jth term occurred in the ith document. The number of rows is equal to the size of the corpus and the number of columns to the size of the vocabulary. The data pre-processing step involves selecting a suitable vocabulary, which corresponds to the columns of the documentterm matrix. Typically, the vocabulary will not be given a-priori, but determined using the available data. The mapping from the document to the term frequency vector involves tokenizing the document and then processing the tokens for example by converting them to lower-case, removing punctuation characters, removing numbers, stemming, removing stop words and omitting terms with a length below a certain minimum. In addition the final document-term matrix can be reduced by selecting only the terms which occur in a minimum number of documents (see Griffiths and Steyvers 2004, who use a value of 5) or those terms with the highest term-frequency inverse document frequency (tf-idf) scores (Blei and Lafferty

Bettina Gr¨n, Kurt Hornik u 2009). The tf-idf scores are only used for selecting the vocabulary, the input data consisting of the document-term matrix uses a term-frequency weighting.

7

2.4. Model selection
For fitting the LDA model or the CTM to a given document-term matrix the number of topics needs to be fixed a-priori. Additionally, estimation using Gibbs sampling requires specification of values for the parameters of the prior distributions. Griffiths and Steyvers (2004) suggest a value of 50/k for α and 0.1 for δ. Because the number of topics is in general not known, models with several different numbers of topics are fitted and the optimal number is determined in a data-driven way. Model selection with respect to the number of topics is possible by splitting the data into training and test data sets. The likelihood for the test data is then approximated using the lower bound for VEM estimation. For Gibbs sampling the log-likelihood is given by    k  V  Γ(V δ) (j) (.)  log(p(w|z)) = k log + log(Γ(nK + δ)) − log(Γ(nK + V δ)) .   Γ(δ)V
K=1 j=1

The perplexity is often used to evaluate the models on held-out data and is equivalent to the geometric mean per-word likelihood. Perplexity(w) = exp − log(p(w))
D d=1 V (jd) j=1 n

n(jd) denotes how often the jth term occurred in the dth document. If the model is fitted using Gibbs sampling the likelihood is determined for the perplexity using
D V k

log(p(w)) = d=1 j=1

n(jd) log
K=1

θK βK

(d) (j)

(see Newman, Asuncion, Smyth, and Welling 2009). The topic weights θK can either be determined for the new data using Gibbs sampling where the term distributions for topics are kept fixed or equal weights are used as implied by the prior distribution. If the perplexity is calculated by averaging over several draws the mean is taken over the samples inside the logarithm. In addition the marginal likelihoods of the models with different numbers of topics can be compared for model selection if Gibbs sampling is used for model estimation. Griffiths and Steyvers (2004) determine the marginal likelihood using the harmonic mean estimator (Newton and Raftery 1994), which is attractive from a computational point of view because it only requires the evaluation of the log-likelihood for the different posterior draws of the parameters. The drawback however is that the estimator might have infinite variance. Different methods for evaluating fitted topic models on held-out documents are discussed and compared in Wallach, Murray, Salakhutdinov, and Mimno (2009). Another possibility for model selection is to use hierarchical Dirichlet processes as suggested in Teh, Jordan, Beal, and Blei (2006).

(d)

8

topicmodels: An R Package for Fitting Topic Models

3. Application: Main functions LDA() and CTM()
The main functions in package topicmodels for fitting the LDA and CTM models are LDA() and CTM(), respectively. R> LDA(x, k, method = "VEM", control = NULL, model = NULL, ...) R> CTM(x, k, method = "VEM", control = NULL, model = NULL, ...) These two functions have the same arguments. x is a suitable document-term matrix with nonnegative integer count entries, typically a "DocumentTermMatrix" as obtained from package tm. Internally, topicmodels uses the simple triplet matrix representation of package slam (Hornik, Meyer, and Buchta 2011) (which, similar to the “coordinate list” (COO) sparse matrix format, stores the information about non-zero entries xij in the form of (i, j, xij ) triplets). x can be any object coercible to such simple triplet matrices (with count entries), in particular objects obtained from readers for commonly employed document-term matrix storage formats. For example the reader read_dtm_Blei_et_al() available in package tm allows to read in data provided in the format used for the code by Blei and co-authors. k is an integer (larger than 1) specifying the number of topics. method determines the estimation method used and currently can be either "VEM" or "Gibbs" for LDA() and only "VEM" for CTM(). Users can provide their own fit functions to use a different estimation technique or fit a slightly different model variant and specify them to be called within LDA() and CTM() via the method argument. Argument model allows to provide an already fitted topic model which is used to initialize the estimation. Argument control can be either specified as a named list or as a suitable S4 object where the class depends on the chosen method. In general a user will provide named lists and coercion to an S4 object will internally be performed. The following arguments are possible for the control for fitting the LDA model with the VEM algorithm. They are set to their default values. R> control_LDA_VEM control_LDA_Gibbs control_CTM_VEM install.packages("corpus.JSS.papers", + repos = "http://datacube.wu.ac.at/", type = "source") R> data("JSS_papers", package = "corpus.JSS.papers") Alternatively, one can harvest JSS publication Dublin Core http://dublincore.org/ metadata (including information on authors, publication date and the abstract) from the JSS web site using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), for which package OAIHarvester (Hornik 2011) provides an R client. R> R> R> R> R> R> + library("OAIHarvester") x library("slam") R> summary(col_sums(JSS_dtm)) Min. 1st Qu. 1.00 1.00 Median 2.00 Mean 3rd Qu. 5.98 4.00 Max. 450.00

R> term_tfidf 0)) R> summary(term_tfidf) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.01520 0.07472 0.09817 0.12210 0.13840 1.16500 R> JSS_dtm = 0.1] R> JSS_dtm 0,] R> summary(col_sums(JSS_dtm)) Min. 1st Qu. 1.000 1.000 Median 1.000 Mean 3rd Qu. 2.763 3.000 Max. 47.000

Bettina Gr¨n, Kurt Hornik u After this pre-processing we have the following document-term matrix with a reduced vocabulary which we can use to fit topic models. R> dim(JSS_dtm) [1] 348 2086

13

In the following we fit an LDA model with 30 topics using (1) VEM with α estimated, (2) VEM with α fixed and (3) Gibbs sampling with a burn-in of 1000 iterations and recording every 100th iterations for 1000 iterations. The initial α is set to the default value. By default only the best model with respect to the log-likelihood log(p(w|z)) observed during Gibbs sampling is returned. In addition a CTM is fitted using VEM estimation. We set the number of topics rather arbitrarily to 30 after investigating the performance with the number of topics varied from 2 to 200 using 10-fold cross-validation. The results indicated that the number of topics has only a small impact on the model fit on the hold-out data. There is only slight indication that the solution with two topics performs best and that the performance deteriorates again if the number of topics is more than 100. For applications a model with only two topics is of little interest because it enables only to group the documents very coarsely. This lack of preference of a model with a reasonable number of topics might be due to the facts that (1) the corpus is rather small containing less than 500 documents and (2) the corpus consists only of text documents on statistical software. R> R> R> R> + + + + + + + + + library("topicmodels") k Topic Terms Terms[,1:5] Topic 1 "network" "lispstat" "mathemat" "learn" "text" Topic 2 "confid" "formula" "interv" "recurs" "cumul" Topic 3 "cluster" "correl" "gee" "region" "boost" Topic 4 "random" "variat" "mantel" "matrices" "uniform" Topic 5 "beta" "captur" "tables" "rcaptur" "file"

15

[1,] [2,] [3,] [4,] [5,]

If any category labelings of the documents were available, these could be used to validate the fitted model. Some JSS papers should have similar content because they appeared in the same special volume. The most likely topic of the papers which appeared in Volume 24 called “Statistical Modeling of Social Networks with ‘statnet”’ is given by R> (topics_v24 most_frequent_v24 terms(jss_TM[["VEM"]], 10)[, most_frequent_v24] [1] [4] [7] [10] "network" "ergm" "format" "econometr" "hydra" "statnet" "exponentialfamili" "graph" "brief" "imag"

Clearly this topic is related to the general theme of the special issue. This indicates that the fitted topic model was successful at detecting the similarity between papers in the same special issue without using this information.

5. Summary
Package topicmodels provides functionality for fitting the topic models LDA and CTM in R. It builds on and complements functionality for text mining already provided by package tm. Functionality for constructing a corpus, transforming a corpus into a document-term matrix and selecting the vocabulary is available in tm. The basic text mining infrastructure provided by package tm is hence extended to allow also fitting of topic models which are seen nowadays as state-of-the-art techniques for analyzing document-term matrices. The

16

topicmodels: An R Package for Fitting Topic Models

advantages of package topicmodels are that (1) it gives access within R to the code written by David M. Blei and co-authors, who introduced the LDA model as well as the CTM in their papers, and (2) allows different estimation methods by providing VEM estimation as well Gibbs sampling. Extensibility to other estimation techniques or slightly different model variants is easily possible via the method argument. Packages Snowball (Hornik 2009) and tm provide stemmers and stop word lists not only for English, but also for other languages. To the authors’ knowledge topic models have so far only been used for corpora in English. The availability of all these tools in R hopefully does not only lead to an increased use of these models, but also facilitates to try them out for corpora in other languages as well as in different settings. In addition different modeling strategies for model selection, such as cross-validation, can be easily implemented with a few lines of R code and the results can be analyzed and visualized using already available tools in R. Due to memory requirements package topicmodels will for standard hardware only work for reasonably large corpora with numbers of topics in the hundreds. Gibbs sampling needs less memory than using the VEM algorithm and might therefore be able to fit models when the VEM algorithm fails due to high memory demands. In order to be able to fit topic models to very large data sets distributed algorithms to fit the LDA model were proposed for Gibbs sampling in Newman et al. (2009). The proposed Approximate Distributed LDA (AD-LDA) algorithm requires the Gibbs sampling methods available in topicmodels to be performed on each of the processors. In addition functionality is needed to repeatedly distribute the data and parameters to the single processors and synchronize the results from the different processors until a termination criterion is met. Algorithms to parallelize the VEM algorithm for fitting LDA models are outlined in Nallapati, Cohen, and Lafferty (2007). In this case the processors are used in the E-step such that each processor calculates only the sufficient statistics for a subset of the data. We intend to look into the potential of leveraging the existing infrastructure for large data sets along the lines proposed in Nallapati et al. (2007) and Newman et al. (2009). The package allows us to fit topic models to different corpora which are already available in R using package tm or can easily be constructed using tools such as the package OAIHarvester. We are also interested in comparing the performance of topic models for clustering documents to other approaches such as using mixtures of von Mises-Fisher distributions to model the term distributions of the documents (Banerjee, Dhillon, Ghosh, and Sra 2005) where the R package movMF (Hornik and Gr¨n 2011) is available on CRAN. u Different variants of topic models have been recently proposed. Some models aim at relaxing the assumption of independence of topics which is imposed by LDA such as the CTM, hierarchical topic models (Blei, Griffiths, Jordan, and Tenenbaum 2003a) or Pachinko allocation (Li and McCallum 2006) and hierarchical Pachinko allocation (Mimno, Li, and McCallum 2007). Another possible extension of the LDA model is to include additional information. Using the time information leads to dynamic topic models (Blei and Lafferty 2006) while using the author information of the documents gives the author-topic model (Rosen-Zvi, Chemudugunta, Griffiths, Smyth, and Steyvers 2010). We are interested in extending the package to cover at least a considerable subset of the different proposed topic models. As a starting point we will use Heinrich (2009) and Heinrich and Goesele (2009) who provide a common framework for topic models which only consist of Dirichlet-multinomial mixture “levels”. Examples for such topic models are LDA, the author-topic model, Pachinko allocation and hierarchical Pachinko allocation.

Bettina Gr¨n, Kurt Hornik u

17

Acknowledgments
We would like to thank two anonymous reviewers for their valuable comments which led to several improvements. This research was supported by the Austrian Science Fund (FWF) under Hertha-Firnberg grant T351-N18 and under Elise-Richter grant V170-N18.

References
Airoldi EM, Blei DM, Fienberg SE, Xing EP (2008). “Mixed Membership Stochastic Blockmodels.” Journal of Machine Learning Research, 9, 1981–2014. Banerjee A, Dhillon IS, Ghosh J, Sra S (2005). “Clustering on the Unit Hypersphere Using von Mises-Fisher Distributions.” Journal of Machine Learning Research, 6, 1345–1382. Blei DM, Griffiths TL, Jordan MI, Tenenbaum JB (2003a). “Hierarchical Topic Models and the Nested Chinese Restaurant Process.” In S Thrun, LK Saul, B Sch¨lkopf (eds.), Advances o in Neural Information Processing Systems 16. MIT Press, Cambridge, MA. Blei DM, Lafferty JD (2006). “Dynamic Topic Models.” In ICML’06: Proceedings of the 23rd International Conference on Machine Learning, pp. 113–120. ACM Press. Blei DM, Lafferty JD (2007). “A Correlated Topic Model of Science.” The Annals of Applied Statistics, 1(1), 17–35. Blei DM, Lafferty JD (2009). “Topic Models.” In A Srivastava, M Sahami (eds.), Text Mining: Classification, Clustering, and Applications. Chapman & Hall/CRC Press. Blei DM, Ng AY, Jordan MI (2003b). “Latent Dirichlet Allocation.” Journal of Machine Learning Research, 3, 993–1022. Chang J (2010). lda: Collapsed Gibbs Sampling Methods for Topic Models. R package version 1.2.3, URL http://CRAN.R-project.org/package=lda. Daum´ III H (2008). HBC: Hierarchical Bayes Compiler. Pre-release version 0.7, URL e http://www.cs.utah.edu/~hal/HBC/. Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990). “Indexing by Latent Semantic Analysis.” Journal of the American Society for Information Science, 41(6), 391–407. Dempster AP, Laird NM, Rubin DB (1977). “Maximum Likelihood from Incomplete Data Via the EM-Algorithm.” Journal of the Royal Statistical Society B, 39, 1–38. Feinerer I (2011). tm: Text Mining Package. R package version 0.5-5., URL http://CRAN. R-project.org/package=tm. Feinerer I, Hornik K, Meyer D (2008). “Text Mining Infrastructure in R.” Journal of Statistical Software, 25(5), 1–54. URL http://www.jstatsoft.org/v25/i05/. Griffiths TL, Steyvers M (2004). “Finding Scientific Topics.” Proceedings of the National Academy of Sciences of the United States of America, 101, 5228–5235.

18

topicmodels: An R Package for Fitting Topic Models

Gr¨n B, Hornik K (2011). “topicmodels: An R Package for Fitting Topic Models.” Journal u of Statistical Software, 40(13), 1–30. URL http://www.jstatsoft.org/v40/i13/. Hall D, Jurafsky D, Manning CD (2008). “Studying the History of Ideas Using Topic Models.” In 2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Proceedings of the Conference, 25-27 October 2008, Honolulu, Hawaii, USA, A Meeting of SIGDAT, a Special Interest Group of the ACL, pp. 363–371. ACL. Heinrich G (2009). “A Generic Approach to Topic Models.” In WL Buntine, M Grobelnik, D Mladenic, J Shawe-Taylor (eds.), Machine Learning and Knowledge Discovery in Databases, volume 5781 of Lecture Notes in Computer Science, pp. 517–532. SpringerVerlag, Berlin. Heinrich G, Goesele M (2009). “Variational Bayes for Generic Topic Models.” In B Mertsching, M Hund, Z Aziz (eds.), KI 2009: Advances in Artificial Intelligence, volume 5803 of Lecture Notes in Computer Science, pp. 161–168. Springer-Verlag, Berlin. Hoffman MD, Blei DM, Bach F (2010). “Online Learning for Latent Dirichlet Allocation.” In J Lafferty, CKI Williams, J Shawe-Taylor, R Zemel, A Culotta (eds.), Advances in Neural Information Processing Systems 23, pp. 856–864. MIT Press, Cambridge, MA. Hofmann T (1999). “Probabilistic Latent Semantic Indexing.” In SIGIR’99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM Press. Hornik K (2009). Snowball: Snowball Stemmers. R package version 0.0-7, URL http: //CRAN.R-project.org/package=Snowball. Hornik K (2011). OAIHarvester: Harvest Metadata Using OAI-PMH v2.0. R package version 0.1-3, URL http://CRAN.R-project.org/package=OAIHarvester. Hornik K, Gr¨n B (2011). movMF: Mixtures of von Mises Fisher Distributions. R package u version 0.0-0, URL http://CRAN.R-project.org/package=movMF. Hornik K, Meyer D, Buchta C (2011). slam: Sparse Lightweight Arrays and Matrices. R package version 0.1-21, URL http://CRAN.R-project.org/package=slam. Li W, McCallum A (2006). “Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations.” In ICML’06: Proceedings of the 23rd International Conference on Machine Learning, pp. 577–584. ACM Press, New York. Li Z, Wang C, Xie X, Wang X, Ma WY (2008). “Exploring LDA-Based Document Model for Geographic Information Retrieval.” In C Peters, V Jijkoun, T Mandl, H M¨ller, D Oard, u AP nas, V Petras, D Santos (eds.), Advances in Multilingual and Multimodal Information Retrieval, volume 5152 of Lecture Notes in Computer Science, pp. 842–849. Springer-Verlag, Berlin. McCallum AK (2002). MALLET: Machine Learning for Language Toolkit. URL http: //mallet.cs.umass.edu/. Microsoft Corporation (2010). Infer.NET User Guide. Version 2.4 beta 2, URL http: //research.microsoft.com/en-us/um/cambridge/projects/infernet/.

Bettina Gr¨n, Kurt Hornik u

19

Mimno D, Li W, McCallum A (2007). “Mixtures of Hierarchical Topics with Pachinko Allocation.” In ICML’07: Proceedings of the 21st International Conference on Machine Learning, pp. 633–640. ACM Press. Mochihashi D (2004a). “A Note on a Variational Bayes Derivation of Full Bayesian Latent Dirichlet Allocation.” Unpublished manuscript, URL http://chasen.org/~daiti-m/ paper/lda-fullvb.pdf. Mochihashi D (2004b). lda, a Latent Dirichlet Allocation Package. MATLAB and C package version 0.1, URL http://chasen.org/~daiti-m/dist/lda/. Nallapati R, Cohen W, Lafferty J (2007). “Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability.” In ICDMW’07: Proceedings of the Seventh IEEE International Conference on Data Mining Workshops, pp. 349–354. IEEE Computer Society, Washington, DC. Newman D, Asuncion A, Smyth P, Welling M (2009). “Distributed Algorithms for Topic Models.” Journal of Machine Learning Research, 10, 1801–1828. Newton MA, Raftery AE (1994). “Approximate Bayesian Inference with the Weighted Likelihood Bootstrap.” Journal of the Royal Statistical Society B, 56(1), 3–48. Nigam K, McCallum AK, Thrun S, Mitchell T (2000). “Text Classification from Labeled and Unlabeled Documents Using EM.” Machine Learning, 39(2–3), 103–134. Phan XH, Nguyen LM, Horiguchi S (2008). “Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-Scale Data Collections.” In Proceedings of the 17th International World Wide Web Conference (WWW 2008), pp. 91–100. Beijing, China. Porteous I, Asuncion A, Newman D, Ihler A, Smyth P, Welling M (2008). “Fast Collapsed Gibbs Sampling for Latent Dirichlet Allocation.” In KDD’08: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 569–577. ACM Press. R Development Core Team (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http: //www.R-project.org/. Rosen-Zvi M, Chemudugunta C, Griffiths T, Smyth P, Steyvers M (2010). “Learning AuthorTopic Models from Text Corpora.” ACM Transactions on Information Systems, 28(1). Steyvers M, Griffiths T (2007). “Probabilistic Topic Models.” In TK Landauer, DS McNamara, S Dennis, W Kintsch (eds.), Handbook of Latent Semantic Analysis. Lawrence Erlbaum Associates. Steyvers M, Griffiths T (2011). MATLAB Topic Modeling Toolbox 1.4. URL http:// psiexp.ss.uci.edu/research/programs_data/toolbox.htm. Teh YW, Jordan MI, Beal MJ, Blei DM (2006). “Hierarchical Dirichlet Processes.” Journal of the American Statistical Association, 101(476), 1566–1581.

20

topicmodels: An R Package for Fitting Topic Models

Temple Lang D (2010). XML: Tools for Parsing and Generating XML Within R and S-PLUS. R package version 3.2-0, URL http://CRAN.R-project.org/package=XML. Wainwright MJ, Jordan MI (2008). “Graphical Models, Exponential Families, and Variational Inference.” Foundations and Trends in Machine Learning, 1(1–2), 1–305. Wallach HM, Murray I, Salakhutdinov R, Mimno D (2009). “Evaluation Methods for Topic Models.” In ICML’09: Proceedings of the 26th International Conference on Machine Learning, pp. 1105–1112. ACM Press. Wei X, Croft WB (2006). “LDA-Based Document Models for Ad-Hoc Retrieval.” In SIGIR’06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 178–185. ACM Press, New York.

Affiliation:
Bettina Gr¨n u Institut f¨r Angewandte Statistik / IFAS u Johannes Kepler Universit¨t Linz a Altenbergerstraße 69 4040 Linz, Austria E-mail: Bettina.Gruen@jku.at URL: http://ifas.jku.at/gruen/ Kurt Hornik Institute for Statistics and Mathematics WU Wirtschaftsuniversit¨t Wien a Augasse 2–6 1090 Wien, Austria E-mail: Kurt.Hornik@R-project.org URL: http://statmath.wu.ac.at/~hornik/

Similar Documents

Free Essay

Student

...Revision of Critical essay *Introduction In today's society there is a lot of pressure on students academically to have a good performance and with that comes a lot of stress. Some students find a way to try to balance their hectic school life style whether it be some kind of recreational activity. One of those activities is sports and whether it can make a better student. I believe that yes it can increase your performance academically because it teaches you skills such as focus, fitness and communication with others. In the article “do athletes make better students, Natalie Gil written for the guardian.com. Natlie Gil claims that studies show that doing both can benefit studies and sports performance, providing motivation and preparation. Natalie Gil also goes on to state that it helps organization and pervents procrastination and that being fit alters students mood in a good way claiming a healthy body is a healthy mind. Lastly, Natalie Gil goes on to show evidence that it also helps with communication and team work whether at school or later in landing a career. Pathos Natalie Gil Appeals to the stress and desire to succeed in today's world as students upcoming in today's society. She also uses the points or appeal to support her view or stance on the subject that athletes do make better students and that this will lead to success not only in their academic life but also in their career choice Logos Natalie...

Words: 616 - Pages: 3

Premium Essay

Student

...are important to be included in the evaluation of teaching effectiveness. These factors are as the criteria for the evaluating of educational effectiveness. Some of these factors still work as a criterion for the evaluation process. While, the other factors have to be excluded from the evaluation and not to be given as much weight. Therefore, the main goal of this study is to ask administrators about which items still valid until the now and have to be included in the evaluation process and which of these items are invalid to be an evaluation criterion. This article also offers the main sources of data for evaluation of faculty performance as one of the important components of evaluation of educational effectiveness. There sources are students’ evaluation tools, teaching portfolios, classroom visitation reports, and scholarship activities. These sources offer significant information about the faculty performance and consequently they will contribute significantly in assessing and evaluating the teaching effectiveness. There are some items of evaluation have to be included and be given more weight in any evaluation process of the educational effectiveness because they have a significant relation to the success of the evaluation process. These items are currency in field, peers evaluation, classroom visits, professors preparations. While, there are some items have to be excluded because they do not contribute in success of evaluation of teaching effectiveness...

Words: 325 - Pages: 2

Free Essay

Student

...SOX testing, I was also assigned to assist building the Compliance Universe for the whole organization. I appropriately allocated my time and energy to these two projects, so that I completed most of my work in a high quality and on a timely basis. I am a dedicated team player who loves communicating with people. I interviewed Hologic’s employees to understand key business processes, joined all the staff meetings and presented my ideas and achievements to the team, collaborated with colleagues to work on other projects to meet the deadline. I am also a person with great research and analytical skills. I used CCH, FASB Codification and some other information sources to finish my cases in academic study. Even though I am an international student, I believe that I am better for this position than anyone else. Companies like Signiant need global perspective people. I majored in International economy and trade during undergraduate study. I have knowledge about foreign currency, international transactions and taxes. All I need is a chance to learn and contribute in a fast-paced company like Signiant. The enclosed resume briefly summarizes my educational background and experiences, I would like to meet with you for an interview during which I can fully express my capacity and desire to work for Signiant. In the meantime, if you need any additional information, please contact me by phone at 781-502-8582 or via e- mal at liulezi2012@hotmail.com Thank you for your time and...

Words: 319 - Pages: 2

Free Essay

Student

...THE RATE OF INVOLVEMENT OF KPTM KL’S STUDENTS IN SPORTS AT THE COLLEGE Prepared by : MUHAMMAD AEZHAD BIN AZHAR CVB130724387 MUHAMMAD FARHAN BIN ABDUL RAHMAN CVB130724287 RAHMAN MUSTAQIM BIN KHOSAIM CVB130724279 MUHAMMAD AIMAN BIN MOHD HUSNI CVB130724388 Prepared for : Madam Jaaz Suhaiza Jaafar Submitted in partial fulfillments of the requirement of the 106km course. TABLE OF CONTENTS NUMBER | CONTENTS | PAGES | 1. | ACKNOWLEDGEMENT | 3 | 2. | INTRODUCTION | 4 | 3. | OBJECTIVES | 5 | 4. | METHODOLOGY | 6-7 | 5. | GRAPH | 8-11 | 6. | CONCLUSION | 12 | 7. | APPENDIX TABLE | 13 | 8. | APPENDIX | 14-17 | ACKNOWLEDGEMENT First of all,we really want to thankful to Madam Jaaz Suhaiza Jaafar because allowed me to do this mini project until we’ve successfully completed it.We want thankful too because madam helped us a lot such as give instructions or order how to make it properly done until we’ve finished it. If we didn’t get help from madam,its really hard to us for completed it in a short time. We also want to very thankful too all our 50 respondents which all of them its from KPTM KL students who was in diploma,degree or professional. They all was nice and very friendly with us and nobody refuse to give a little time to fill up our questionnaire. We really want to wish thanked you so much because without them we can’t finished our mini project. Last but not least,thank you so much too our...

Words: 2116 - Pages: 9

Free Essay

Student

...Study of Asia-Pacific MBA Programs Bloomberg Business week posted an article on March 17th 2014 titled, Elite Business Schools Hike Tuition for the Class of 2016. This article draws a comparison between tuition costs for the class of 2015 for selected US MBA programs and the class of 2016. Tuition costs are increasing more and more every year, for this reason looking at other alternatives may be more cost effective. The following study provides and interpretation of tuition cots both local and foreign in the Asia-Pacific region. From this study we can see the comparison between tuition costs and starting salaries. We can also see other deciding factors such as admission requirements. Finally this study provides a recommendation for an MBA program in the Asia-Pacific region. Please note Table 1.1 listing the study’s programs with their correlating graph ID. Table 1.1 Business School | Graph ID | Lahore University of Management Sciences | LUMS | Indian Institute of Management (Calcutta) | IIMC | University of New South Wales (Sydney) | UNSW | Indian Institute of Management (Bangalore) | IIMB | Curtin Institute of Technology (Perth) | CIT | Massey University (Palmerston North, New Zealand) | MU | University of Queensland (Brisbane) | UQ | University of Adelaide | UA | Monash Mt. Eliza Business School (Melbourne) | MMEBS | Melbourne Business School | MBS | Royal Melbourne Institute of Technology | RMIT | Macquarie Graduate School of Management...

Words: 3907 - Pages: 16

Premium Essay

Student

...playing a basic rule in the education, and the government was searching for a solution to eliminate this phenomenon. They found that establish public schools overall the states will improve a lot of the poor income people to be introduced in the educational field, and over the years will produce community with cultured educated society. The education is varies in all levels, starting from preschool reaching to postgraduate like masters and doctoral degree. The insurance of improvement in education that any non U.S graduate must have multiple exams prior to admission e.g. TOEFL, ILETS, GRE, GMAT. Nowadays there are gradual increase in the numbers of international students want to continue their educations in United States. The improvement of the education in United States is very obvious and attracts the students worldwide, and they release a lot of plans in progress. All the opportunities social, health, economic, academic will depend on the basic structure...

Words: 306 - Pages: 2

Free Essay

Student

...Retention(n), retain verb (used with object) the ​continued use, ​existence, or ​possession of something or someone:Two ​influential ​senators have ​argued for the retention of the ​unpopular ​tax.The retention of ​old ​technology has ​slowed the company's ​growth.​water/​heat retention Particularly(adv) Especially(adv) Deter(v) to make someone less likely to do something, or to make something less likely to happen caydırmak, vazgeçirmek, yıldırmak Perception(n) BELIEF [C]› what you think or believe about someone or something algılama, sezgi, görme The public perception of him as a hero is surprising. NOTICE [U] the ability to notice something fark etme, farkına varma, tanıma, görme Alcohol reduces your perception of pain. Conationimpulse Unanimous agreed by everyoneoy birliği ile üzerinde uzlaşılan; herkesçe kabul edilen; genel kabul görenThe jury was unanimous in finding him guilty. unanimity     /ˌjuːnəˈnɪməti/ noun [U]› when everyone agrees about somethinggenel/toplumsal uzlaşı; oy birliği ile anlaşma; genel kabul; fikir birliğiunanimously adverb›oy birliği ile kabul edilmişThe members unanimously agreed to the proposal. dissonancenoun [U]  UK   /ˈdɪs.ən.əns/  US   /ˈdɪs.ə.nəns/      › specialized music a ​combination of ​sounds or ​musical ​notes that are not ​pleasant when ​heard together:the ​jarring dissonance of Klein's ​musical ​score› formal ​disagreement dissonant adjective UK   /ˈdɪs.ən.ənt/  US   /ˈdɪs.ə.nənt/ specializedor formal ›a dissonant ​combination of...

Words: 335 - Pages: 2

Premium Essay

Student

...Student Handbook 2015/2016 www.praguecollege.cz Table of Contents Introduction Message from the Director Mission, Vision and Values Why study at Prague College Admissions A short guide to Prague College qualifications English for Higher Education Foundation Diploma in Business Foundation Diploma in Computing Foundation Diploma in Art & Design Professional Diplomas in Business Professional Diplomas in Computing Higher National Diploma BA (Hons) International Business Management BA (Hons) International Business Management (Flexible Study Programme) BA (Hons) Business Finance & Accounting BA (Hons) Graphic Design BA (Hons) Fine Art Exp. Media BSc (Hons) Computing BA (Hons) Communications & Media Studies MSc International Management MSc Computing Accreditation & Validation UK/Pearson Credit system Transfer of credits Student support Accommodation Study Advising and Support Financial support Visas for foreign students Scholarships Benefits for students Study abroad Internships Assistance in employment Counselling Centre Student Resources Computer labs Online Learning Centre (Moodle) Prague College email Physical library Digital Library ISIFA Images Textbooks and class materials Graphic Design/Interactive Media/Fine Art materials and costs Personal computers Message boards and digital signs Newsletters Open lectures, seminars and events Student ID cards Centre for Research and Interdisciplinary Studies (CRIS) Prague...

Words: 27092 - Pages: 109

Free Essay

International Student

...[pic] TOPIC: INTERNATIONAL STUDENTS’ ATTITUDES ABOUT HIGHER EDUCATION IN THE UK Student: Pham Trang Huyen My Student ID: 77142444 10 weeks Pre-sessional course December, 2013 List of content Abstract 3 1. Introduction 4 2. Literature review 5 2.1. Higher Education in the UK 5 2.2. Teacher-student relationships and the quality of teaching 5 2.3. Different learning styles 6 2.4. Group work 7 2.5. Financial issues 8 3. Methodology 9 4. Results 10 5. Discussion 14 6. Conclusion 16 List of References 17 Appendix 19 Abstract Higher education is a competitive business which produces huge benefits for the UK economy. This paper reveals international students’ attitudes about UK higher education and focuses on direct factors which can affect students’ opinions. Reports of international students’ attitudes already carried out in Leeds Metropolitan University are analyzed and the main findings are emphasized. A total of eighteen international students interviewed provided data on their experience in UK education that involves the challenges they have faced and what they have achieved. The project concludes that not only UK tuition fees but also the quality of education can affect international students’ decision to study in the UK. Therefore measures should be taken in...

Words: 3732 - Pages: 15

Free Essay

Working Student

...INTRODUCTION Many students of HRM in Taguig City University work part-time Employment during school could improve grades if working promotes aspects that correspond with academic success, such as industriousness or time management skills, or instead reduce grades by reducing time and energy available for school work. Otherwise, working might be associated with academic performance, yet not directly influence it, if unobserved student differences influence both labor supply and grades. Unmotivated students might neither work for pay nor receive good grades because they put little effort into the labor market or school. In contrast, HRM students uninterested in academics might work long hours that would otherwise have been devoted to leisure. Students might misjudge the link between college achievement and future earnings when making labor supply decisions. If so, obtaining a consistent estimate of how such decisions affect academic performance is prospectively important for policy consideration. Some of HRM students in Taguig City University Students are more likely to work than they are to live on campus, to study full time, to attend a four-year college or university, or to apply for or receive financial aid. Students work regardless of the type of institution they attend, their age or family responsibilities, or even their family income or educational and living expenses. Most HRM students at Taguig City University face many challenges in their already busy everyday lives...

Words: 2898 - Pages: 12

Free Essay

Student Adversity

... Adversity allows an individual to develop a sense of discipline, as well as encouraging individuals to exercise their mind to confront a problem or conflict. Specifically, students who encounter hardships are more inclined to try harder, which promotes competition within the school. Although adversity may be beneficial towards some students, challenges can be detrimental for students who lack confidence. For instance, some students develop a mentality of despair; they believe that if one has to work hard, then the person does not have the natural ability for the assignment. Based on the effects of adversity aforementioned, I believe that students can both benefit from the obstacles faced in school with the proper mentality or the effects could be hindering. Students face adversity every day, regardless of how transparent the obstacle may be; some problems may not be as evident as others. According to Carol S. Dweck, author of Brainology, all students face adversities throughout their high-school career, specifically, the challenge of overcoming a fixed mindset. In this excerpt, “The belief that intelligence is fixed dampened students’ motivation to learn, made them afraid of effort, and made them want to quit after a setback”, Carol portrays the illusion that students have over intuitive intelligence (Dweck 2). Students who share this belief of a...

Words: 1029 - Pages: 5

Free Essay

Student Handbook

...Student Handbook (Procedure & Guideline) for Undergraduate Programmes 2014 Revised: April 2014 UCSI Education Sdn. Bhd. (185479-U) VISION AND MISSION STATEMENT OF UCSI UNIVERSITY VISION STATEMENT To be an intellectually resilient praxis university renowned for its leadership in academic pursuits and engagement with the industry and community MISSION STATEMENT  To promote transformative education that empowers students from all walks of life to be successful individuals with integrity, professionalism and a desire to contribute to society  To optimize relationships between industry and academia through the provision of quality education and unparalleled workplace exposure via Praxis Centres  To spearhead innovation in teaching and learning excellence through unique delivery systems  To foster a sustainable culture of research, value innovation and practice, in partnership with industries and society  To operate ethically at the highest standards of efficiency, while instilling values of inclusiveness, to sustain the vision for future generations 2 UCSI Education Sdn. Bhd. (185479-U) Graduate Attributes Getting a university degree is every student‟s ultimate dream because it opens doors to career opportunities anywhere in the world. A university degree is proof of one‟s intellectual capacity to absorb, utilize and apply knowledge at the workplace. However, in this current competitive world, one‟s knowledge and qualifications...

Words: 28493 - Pages: 114

Premium Essay

Student Policy

...Student Academic Policies Computer Usage: Sullivan University Systems (SUS) provides computer networking for all staff, students and anyone else affiliated with the university community. Sullivan University will provide a platform that is conducive for learning while maintain and respecting the user privacy. Users are authorized to use the accounts only. Passwords should be protected, please keep the confidential (Computer Usage. (2012) Sullivan University. Student Handbook 2012-2013, pp. 12-14.). While using the SUS users have a responsibility and are expected to follow some key rules: 1. Do not abuse the equipment 2. Computers must be used for course work 3. No unauthorized down loading 4. At no time will user install software of any kind Disciplinary action for violations of the Computer usage of policy will be enforced and are as follows: 1. Loss of computer privileges 2. Disconnection from the network 3. Expulsion 4. Prosecution The Compute usage policy is standard and pretty straight forward. The statement lets students know what is and is not proper usage. What I would have like to have seen is a social media portion in the usage policy. Academic Integrity: Cheating and Plagiarism is a violation of the University’s Academic Integrity Policy. All students are expected to submit their own work. Penalties for those who are found guilty of cheating may include: (Academic Integrity. (2014, January 1) Sullivan University. Sullivan University 2014 Catalog...

Words: 320 - Pages: 2

Premium Essay

Student Satisfaction

...between the quality of school facilities and student...

Words: 2174 - Pages: 9

Premium Essay

Working Students

...performance of hiring working students Introduction While most students have parents that can support them, there are those students that need get what you call a “part-time job” to help their parents that can’t support them all the way. However, being employed and being a student can be too much to a person. The business process outsourcing industry in the Philippines has grown 46% annually since 2006. In its 2013 top 100 ranking of global outsourcing destinations. Significance of the Study There are situations in the life when one must do what they can to achieve their dreams or help their families. Especially if dealt with financial difficulties and there is a need work while studying. They also need to deal with their everyday busy schedules. This research aims to help understand and discuss the issues and concerns of the employed students to benefit the following: Working Students – Being an employee and student at the same time takes a lot of hard work. It can be rigorous but also rewarding especially if you helped your parents. It can also be a good working experience for them for their future. This study will assist them to see the behaviors that help them achieve their professional skills. Scope and Limitations This is study is conducted at the LPU-Manila and the information is viewed only in the light of the particular student and his or her experience as working student. It does not reflect the view of the general working student population or that of other...

Words: 606 - Pages: 3