[PDF] Top 20 Analyzing Bayesian Crosslingual Transfer in Topic Models
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Analyzing Bayesian Crosslingual Transfer in Topic Models
... of crosslingual transfer learning in topic ...on-site transfer as circular validation, and derive an up- per bound based on PAC-Bayesian theories (Sec- tion ...knowledge ... See full document
15
Bilingual LSA Based LM Adaptation for Spoken Language Translation
... for crosslingual LM adaptation in spoken language ...LSA models in which a one-to-one topic correspondence is enforced between the LSA models through the sharing of variational Dirich- let ... See full document
8
Crosslingual and Multilingual Construction of Syntax Based Vector Space Models
... multilingual topic models for news ag- ...to transfer selectional preferences and senti- ment ...for crosslingual information retrieval (Potthast et ... See full document
14
Building Blocks for Variational Bayesian Learning of Latent Variable Models
... The key idea behind developing these blocks is that after the connections between the blocks in the chosen model have been fixed (that is, a particular model has been selected and specified), the cost function and the ... See full document
47
Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling
... in transfer func- tion based regression models that learn a linear or nonlinear mapping between large scale predictors and regional scale predictand ...Regression models are conceptually the simplest ... See full document
13
Analyzing Quantitative Models
... assumptions reasonable and comprehensive? The second stage relates the model's assumptions to the final form of the model. Does the model follow logically from the assumptions? Is it possible for a mathematician to ... See full document
13
Distributed Algorithms for Topic Models
... learning dynamics: taking a few small steps near the starting point, moving up to the true solution, and then sampling near the posterior mode for the rest of the iterations. For each Gibbs iteration, the parameters ... See full document
28
Shared Components Topic Models
... Components Topic Model (SCTM), which addresses both of these issues by generating each topic as a normalized product of a smaller number of underlying ...new topic from scratch, we model a set of ... See full document
10
Authorship Attribution with Topic Models
... of topic-based author representations that go beyond traditional authorship ...three topic models (LDA, AT, and DADT) for several scenarios where the number of authors varies from three to about ... See full document
42
Unsupervised Topic Modelling for Multi Party Spoken Discourse
... Meetings often include off-topic dialogue, in par- ticular at the beginning and end, where infor- mal chat and meta-dialogue are common. Gal- ley et al. (2003) annotated these sections explic- itly, together with ... See full document
8
Inducing Crosslingual Distributed Representations of Words
... cast crosslingual distributed representation induction as a multitask learning problem by treating each word w in our languages’ vocabularies as a separate ... See full document
16
A Method of Accounting Bigrams in Topic Models
... The paper describes the results of an empir- ical study of integrating bigram collocations and similarities between them and unigrams into topic models. First of all, we propose a novel algorithm PLSA-SIM ... See full document
9
International Journal of Computer Science and Mobile Computing
... DIFFERENTIAL TOPIC MODELS 231 ...each topic and the topic sharing between ...the topic-word distribution are defined as Gaussian and encoded with domain ...the topic-word ... See full document
14
Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks
... of Bayesian Knowledge Tracing and Dynamic Bayesian Networks for analyzing the process data of a digital game-based assessment, called Raging Skies (Chu and Chiang, 2018; Chu et ... See full document
21
Statistical Models for Topic Segmentation
... To reiterate, we used our word frequency model with a total of 3 parameters trained from English newswire text to segment Spanish broadcast news data.. regions of a document unless those[r] ... See full document
8
Self disclosure topic model for classifying and analyzing Twitter conversations
... a topic model can be used to identify self-disclosure, but that work applies a two-step process in which a basic topic model is first applied to find the top- ics, and then the topics are post-processed for ... See full document
11
Portfolio Sensitivity Model for Analyzing Credit Risk Caused by Structural and Macroeconomic Changes
... use Bayesian networ- ks. The reasons for that lie in the fact that Bayesian networks are much more powerful to follow the synergy between variables than logistic regression, and Bayesian network ... See full document
16
Automatic Labelling of Topic Models
... top-5 topic term candidate set is the lowest performer out of the three subsets across all four corpora, in terms of both upper bound and the results for the super- vised ...the topic word selection method ... See full document
10
Improvements to the Bayesian Topic N Gram Models
... To address the first problem, we investigate incor- porating a global language model for ease of sparse- ness, along with some priors on a suffix tree to cap- ture the difference of topicality for each context, which ... See full document
11
Bayesian Unsupervised Topic Segmentation
... unsupervised topic segmentation, it is clear that there are other important indicators that are ignored by the current generation of unsupervised ...supervised topic segmentation (Passonneau and Litman, ... See full document
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