124 research outputs found
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Augmenting Naive Bayes Classifiers with Statistical Language Models
We augment naive Bayes models with statistical n-gram language models to address short- comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we re- fer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the indepen- dence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straight- forward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text clas- sification problems—authorship attribution, text genre classification, and topic detection—in several languages—Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model
Modeling Dependent Structure for Utterances in ASR Evaluation
The bootstrap resampling method has been popular for performing significance
analysis on word error rate (WER) in automatic speech recognition (ASR)
evaluation. To deal with dependent speech data, the blockwise bootstrap
approach is also introduced. By dividing utterances into uncorrelated blocks,
this approach resamples these blocks instead of original data. However, it is
typically nontrivial to uncover the dependent structure among utterances and
identify the blocks, which might lead to subjective conclusions in statistical
testing. In this paper, we present graphical lasso based methods to explicitly
model such dependency and estimate uncorrelated blocks of utterances in a
rigorous way, after which blockwise bootstrap is applied on top of the inferred
blocks. We show the resulting variance estimator of WER in ASR evaluation is
statistically consistent under mild conditions. We also demonstrate the
validity of proposed approach on LibriSpeech dataset
Pattern Division Multiple Access with Large-scale Antenna Array
In this paper, pattern division multiple access with large-scale antenna
array (LSA-PDMA) is proposed as a novel non-orthogonal multiple access (NOMA)
scheme. In the proposed scheme, pattern is designed in both beam domain and
power domain in a joint manner. At the transmitter, pattern mapping utilizes
power allocation to improve the system sum rate and beam allocation to enhance
the access connectivity and realize the integration of LSA into multiple access
spontaneously. At the receiver, hybrid detection of spatial filter (SF) and
successive interference cancellation (SIC) is employed to separate the
superposed multiple-domain signals. Furthermore, we formulate the sum rate
maximization problem to obtain the optimal pattern mapping policy, and the
optimization problem is proved to be convex through proper mathematical
manipulations. Simulation results show that the proposed LSA-PDMA scheme
achieves significant performance gain on system sum rate compared to both the
orthogonal multiple access scheme and the power-domain NOMA scheme.Comment: 6 pages, 5 figures, this paper has been accepted by IEEE VTC
2017-Sprin
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