[PDF] Top 20 A Hierarchical Bayesian Language Model Based On Pitman Yor Processes

... Pitman-Yor processes produce power-law dis- tributions that more closely resemble those seen in natural languages, and it has been argued that as a result they are more suited to applications in ... See full document

... including Bayesian non-parametric HMMs (Goldwater and Griffiths, 2007), Pitman-Yor language models (Teh, 2006b; Goldwater et ...non-parametric Bayesian HMM which avoids overfitting, ... See full document

... topic model for the analysis of a new or ill-understood corpus, these lists can be insuffi- ciently ...and model-based reinforcement learning alongside the unigrams would be much more en- ...topic ... See full document

... Markov model where the units (=words) evolve as the Markov process, and each unit has subunits (=characters) that also evolve as the Markov ...gram model on its own, here a word ∞-gram lan- guage ... See full document

... In hierarchical phrase-based machine translation, a rule table is automatically learned by heuristically extracting syn- chronous rules from a parallel ...a hierarchical back-off model for ... See full document

... on Bayesian non-parametric mod- els to learn morphology of ...2007) based on Min- imum Description Length principle is the ref- erence model for highly inflecting languages, such as ...tion ... See full document

... approach based around ITG using a Pitman-Yor Process ...the model and inference, and critically, this is borne out in our experiments where we show uniform improvements in transla- tion ... See full document

... n-gram model. Secondly, the Pitman-Yor process prior (Pitman and Yor, 1997) generates distribu- tions that are well-suited to a variety of power- law behaviours, as is often observed in ... See full document

... 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 include an unsupervised extension of the dou- bly ... See full document

... a Bayesian non-parametric approach which uses hierarchical Pitman-Yor (PY) processes (Pitman, 2002) to model statistical dependencies between predicate and ar- gument ... See full document

... In this paper, we focus on an OSS developed under open source project. We discuss the method of component- oriented software reliability assessment considering the fault-detection rate of each component based on ... See full document

... of model class selection, that is, based on system data, choosing the most plausible model class from a set of competing candidate model classes to represent the behavior of the system of ...A ... See full document

... Since these dependent words can be determined if we parse ’the tennis ball’ into an intermediate struc- ture as shown in Figure 1, the only remaining prob- lem is how to achieve such kind of structure from any sequence. ... See full document

... a Bayesian nonpara- metric model based on hierarchical Dirichlet pro- cess for incorporation of entity embeddings from external knowledge graphs into topic ...a Bayesian nonparameteric ... See full document

... performing model for this problem, based on the use of a hierarchical Bayesian ...the model instead links them via a hierarchical Bayesian global ...sequence model ... See full document

... parametric Bayesian models of language simply and readably using context free ...procedure based on Markov Chain Monte Carlo techniques that makes parameter estimation straightforward for all models ... See full document

... stochastic processes within single cells and can be modeled through stochastic reaction networks ...switch model provides a generic framework to capture many different dynamic features observed in single ... See full document

... a hierarchical Bayesian model arising out of the convolution of stationary processes centered at various lo- ...our model consists of a variable number of parameters and we use the ... See full document

... unsupervised Bayesian model the lim- ited size of this training set constitutes an ad- ditional ...our model to small data sets, we emphasize that the model is generally widely applicable for ... See full document

... In the experiments, we have applied our proposed ap- proach to carefully analyse the performances of two well- known supervised machine learning algorithms that are widely used for real-world text classification tasks: ... See full document