Clinical data annotation has been one of the major obstacles for applying machine learning approaches in clinical NLP. Open-source tools such as NegBio and CheXpert are usually designed on data from specific institutions, which limit their applications to other institutions due to the differences in writing style, structure, language use as well as label definition. In this paper, we propose a new weak supervision annotation framework with two improvements compared to existing annotation frameworks: 1) we propose to select representative samples for efficient manual annotation; 2) we propose to auto-annotate the remaining samples, both leveraging on a self-trained sentence encoder. This framework also provides a function for identifying inconsistent annotation errors. The utility of our proposed weak supervision annotation framework is applicable to any given data annotation task, and it provides an efficient form of sample selection and data auto-annotation with better classification results for real applications.

This article focuses on extracting information from the octave power spectra of long-term constant-Q transform (CQT) for spoofing attack detection. A novel framework based on multi-level transform (MLT) is proposed that can capture the relevant information from octave power spectra using level by level in a multi-level manner. We then derive a novel feature referred to as constant-Q multi-level coefficient (CMC) based on proposed MLT. The proposed feature is evaluated on synthetic as well as replay speech detection studies on ASVspoof 2015 and ASVspoof 2017 version 2.0 database, respectively. We find the proposed CMC feature outperforms the conventional constant-Q cepstral coefficient based long-term feature obtained from linear power spectrum after uniform resampling. This depicts the usefulness of MLT to extract salient artifacts from octave power spectrum. Further, the proposed CMC feature performs better than the existing the well known other state-of-the-art systems for spoofing attack detection that showcases its importance.

Previous studies on Thai Sentence Boundary Detection (SBD) mostly assumed sentence ends at a space disambiguation problem, which classified space either as an indicator for Sentence Boundary (SB) or non-Sentence Boundary (nSB). In this paper, we propose a word labeling approach which treats space as a normal word, and detects SB between any two words. This removes the restriction for SB to be oc-curred only at space and makes our system more robust for modern Thai writing. It is because in modern Thai writing, space is not consistently used to indicate SB. As syntactic information contributes to better SBD, we further propose a joint Part-Of-Speech (POS) tagging and SBD framework based on Factorial Conditional Random Field (FCRF) model. We compare the performance of our proposed ap-proach with reported methods on ORCHID corpus. We also performed experiments of FCRF model on the TaLAPi corpus. The results show that the word labelling approach has better performance than pre-vious space-based classification approaches and FCRF joint model outperforms LCRF model in terms of SBD in all experiments.

In this paper, we argue that for a C-class classification problem, C 2-class classifiers, each of which discriminating one class from the other classes and having a characteristic input feature subset, should in general outperform, or at least match the performance of, a C-class classifier with one single input feature subset. For each class, we select a desirable feature subset, which leads to the lowest classification error rate for this class using a classifier for a given feature subset search algorithm. To fairly compare all models, we propose a weight method for the class-dependent classifier, i.e., assigning a weight to each model's output before the comparison is carried out. The method's performance is evaluated on two artificial data sets and several real-world benchmark data sets, with the support vector machine (SVM) as the classifier , and with the RELIEF, class separability, and minimal-redundancy-maximal-relevancy (mRMR) as attribute importance measures. Our results indicate that the class-dependent feature subsets found by our approach can effectively remove irrelevant or redundant features, while maintaining or improving (sometimes substantially ) the classification accuracy, in comparison with other feature selection methods.

BACKGROUND: Since the single nucleotide polymorphisms (SNPs) are genetic variations which determine the difference between any two unrelated individuals, the SNPs can be used to identify the correct source population of an individual. For efficient population identification with the HapMap genotype data, as few informative SNPs as possible are required from the original 4 million SNPs. Recently, Park et al. (2006) adopted the nearest shrunken centroid method to classify the three populations, i.e., Utah residents with ancestry from Northern and Western Europe (CEU), Yoruba in Ibadan, Nigeria in West Africa (YRI), and Han Chinese in Beijing together with Japanese in Tokyo (CHB+JPT), from which 100,736 SNPs were obtained and the top 82 SNPs could completely classify the three populations. RESULTS: In this paper, we propose to first rank each feature (SNP) using a ranking measure, i.e., a modified t-test or F-statistics. Then from the ranking list, we form different feature subsets by sequentially choosing different numbers of features (e.g., 1, 2, 3, ..., 100.) with top ranking values, train and test them by a classifier, e.g., the support vector machine (SVM), thereby finding one subset which has the highest classification accuracy. Compared to the classification method of Park et al., we obtain a better result, i.e., good classification of the 3 populations using on average 64 SNPs. CONCLUSION: Experimental results show that the both of the modified t-test and F-statistics method are very effective in ranking SNPs about their classification capabilities. Combined with the SVM classifier, a desirable feature subset (with the minimum size and most informativeness) can be quickly found in the greedy manner after ranking all SNPs. Our method is able to identify a very small number of important SNPs that can determine the populations of individuals.

In this paper we propose a novel support vector machine (SVM) with class-dependent features. According to an importance measure, e.g., the RELIEF weight measure or class separability measure, we rank the features importance for each class against the rest of classes. For each class we select an optimal feature subset using a classifier, e.g., the support vector machine (SVM). For the classification on these class-dependent feature subsets, we propose to construct a novel SVM using "one-against-all" in 2 processes: (1) construct one model for each class by training the classifier with the class's optimal feature subset; (2) during testing, each test pattern is tested on all models and the model with the maximum output decides the class of the test pattern. The method's performance is evaluated on two benchmark datasets. Our results indicate that our novel SVM classifier can effectively realize the classification of class-dependent feature subsets found by our wrapper approach which can remove irrelevant features for each class and at the same time maintain or even improve the classification accuracy in comparison with other feature selection methods.