Statistical hypotheses

Xem 1-19 trên 19 kết quả Statistical hypotheses
  • We introduce a novel search algorithm for statistical machine translation based on dynamic programming (DP). During the search process two statistical knowledge sources are combined: a translation model and a bigram language model. This search algorithm expands hypotheses along the positions of the target string while guaranteeing progressive coverage of the words in the source string. We present experimental results on the Verbmobil task.

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  • This paper presents hypothesis mixture decoding (HM decoding), a new decoding scheme that performs translation reconstruction using hypotheses generated by multiple translation systems.

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  • We describe Akamon, an open source toolkit for tree and forest-based statistical machine translation (Liu et al., 2006; Mi et al., 2008; Mi and Huang, 2008). Akamon implements all of the algorithms required for tree/forestto-string decoding using tree-to-string translation rules: multiple-thread forest-based decoding, n-gram language model integration, beam- and cube-pruning, k-best hypotheses extraction, and minimum error rate training.

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  • Word and n-gram posterior probabilities estimated on N-best hypotheses have been used to improve the performance of statistical machine translation (SMT) in a rescoring framework. In this paper, we extend the idea to estimate the posterior probabilities on N-best hypotheses for translation phrase-pairs, target language n-grams, and source word reorderings. The SMT system is self-enhanced with the posterior knowledge learned from Nbest hypotheses in a re-decoding framework.

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  • This paper describes a novel method for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The outputs are combined and a possibly new translation hypothesis can be generated. Similarly to the well-established ROVER approach of (Fiscus, 1997) for combining speech recognition hypotheses, the consensus translation is computed by voting on a confusion network.

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  • his monograph presents methods for full comparative distributional analysis based on the relative distribution. This provides a general integrated framework for analysis, a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesis-driven summary measures, and the potential for decomposition - enabling the examination of complex hypotheses regarding the origins of distributional changes within and between groups.

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  • As science, ecology is often accused of being weak because of its basic lack of predictive power (Peters 1991) and the many ecological concepts judged vague or tautological (Shrader-Frechette and McCoy 1993). Also, important paradigms that dominated the ecological scene for years have been discarded in favor of new concepts and theories that swamp the most recent ecological literature (e.g., the abandoning of the island biogeography theory in favor of the metapopulations theory; Hanski and Simberloff 1997).

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  • In this chapter you will: Develop an understanding of the importance and nature of quality control checks, understand the data entry process and data entry alternatives, learn how surveys are tabulated and cross-tabulated, understand the concept of hypothesis development and how to text hypotheses.

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  • Chapter 7 – Hypothesis testing. This chapter include objectives: Define a hypothesis and describe the steps of hypothesis testing, define a hypothesis and describe the steps of hypothesis testing, distinguish between one-tailed and two-tailed tests of hypotheses,...

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  • C H A P T E R 17 High performance – statistical inference for comparing population means and bivariate data Chapter objectives This chapter will help you to: ■ test hypotheses on the difference between two population means using independent samples and draw appropriate conclusions ■ carry out tests of hypotheses about the difference between two population means using paired data and draw appropriate conclusions ■ test differences between population means using analysis of variance analysis (ANOVA) and draw appropriate conclusions ...

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  • Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means. Statistics Versus Parameters • Statistics: values that are computed from information provided by a sample • Parameters: values that are computed from a complete census which are considered to be precise and valid measures of the population

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  • Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors.

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  • This difference is substantial and highly statistically significant in all specifications. These results are consistent with two different hypotheses. First, unobservable factors at the management company level could be associated with both the decision to specialize in SRI funds and higher fees and performance. In this case, socially responsible investing itself would not have any effect on performance or fees.

    pdf0p khanhchilam 29-03-2013 28 7   Download

  • Chapter 15 APPROXIMATING THE DISTRIBUTIONS OF ECONOMETRIC ESTIMATORS AND TEST STATISTICS THOMAS J. ROTHENBERG The comparison of different hypotheses, i.e. of competing models, is the basis of model specification. It may be performed along two main lines.

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  • If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses.

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  • Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of-theart Statistical Machine Translation (SMT) systems. The algorithms were originally developed to work with N -best lists of translations, and recently extended to lattices that encode many more hypotheses than typical N -best lists. We here extend lattice-based MERT and MBR algorithms to work with hypergraphs that encode a vast number of translations produced by MT systems based on Synchronous Context Free Grammars.

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  • We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction grammar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the translation hypothesis space can be performed in polynomial time, and robustness heuristics arise naturally from a language-independent inversiontransduction model. ...

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  • This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the application being the recovery of named-entity boundaries in a corpus of web data. The first approach uses a boosting algorithm for ranking problems. The second approach uses the voted perceptron algorithm. Both algorithms give comparable, significant improvements over the maximum-entropy baseline. The voted perceptron algorithm can be considerably more efficient to train, at some cost in computation on test examples.

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  • We use a machine learner trained on a combination of acoustic and contextual features to predict the accuracy of incoming n-best automatic speech recognition (ASR) hypotheses to a spoken dialogue system (SDS). Our novel approach is to use a simple statistical User Simulation (US) for this task, which measures the likelihood that the user would say each hypothesis in the current context. Such US models are now common in machine learning approaches to SDS, are trained on real dialogue data, and are related to theories of “alignment” in psycholinguistics.

    pdf9p bunthai_1 06-05-2013 20 1   Download


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