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