Semi-supervised learning approach for Indonesian Named Entity Recognition (NER) using co-training algorithm

A semi-supervised deep learning approach for cropped image detection

Named entity recognition (NER) application development for under-resourced (i.e. NLP resource) language is usually obstructed by lack of named entity tagged dataset and this led to performance deterioration. Similarly, in Amharic language getting annotated training dataset for named entity recognition problem is extortionate, though an enormous amount of untagged data is easily accessible. Fortunately, the performance of NER possibly be boosted via encompassing a few labeled data with an oversized collection of unlabeled data. Based on this premise, this paper tend to investigate graph-based label propagation algorithm for the Amharic NER problem, a simple semi-supervised, iterative algorithm, to propagate labels through the dataset. In addition, it is aimed at making a rigorous comparison with expectation–maximization with semi-supervised learning approaches. The experiment reveals, label propagation based NER achieves superior performance compared to expected maximization using a few labeled training data. Since expectation maximization algorithm demands a moderate labeled example to be learned, meant very few labeled examples are not enough to generate adequate parameters for recognition of named entities, consequently it couldn’t perform great as the label propagation algorithm.

As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets.

In real-world cases, handling both labeled and unlabeled data has raised the interest of several Data Scientists and Machine Learning engineers, leading to several demonstrations that apply data-augmenting approaches in order to obtain a robust and, at the same time, accurate enough learning behavior. The main reason is the existence of much unlabeled data that are ignored by conventional supervised approaches, reducing the chance of enriching the final formatted hypothesis. However, the majority of the proposed methods that operate using both kinds of these data are oriented toward exploiting only one category of these algorithms, without combining their strategies. Since the most popular of them regarding the classification task are Active and Semi-supervised Learning approaches, we aim to design a framework that combines both of them trying to fuse their advantages during the main core of the learning process. Thus, we conduct an empirical evaluation of such a combinatory approach over three problems, which stem from various fields but are all tackled through the use of acoustical signals, operating under the pool-based scenario: gender identification, emotion detection and automatic speaker recognition. Into the proposed combinatory framework, which operates under training sets with small cardinality, our results prove the benefits of adopting such kind of semi-automated approaches regarding both the achieved predictive correctness when reduced consumption of resources takes place, as well as the smoothness of the learning convergence. Several learners have been examined for reaching to more general conclusions, and a variant of self-training scheme has been also examined.

High-dimensional semi-supervised learning via a fusion-refinement procedure

Gathering labeled data is one of the most time-consuming and expensive tasks in supervised machine learning. In practical applications, there are usually quite limited labeled training samples but abundant unlabeled data that is easy to collect. Semi-supervised learning and active learning are two important techniques for learning a discriminative classification model when labeled data is scarce. However, unlabeled data with significant noises and outliers cannot be well exploited and usually worsen the performance of semi-supervised learning and the performance of active learning also needs a powerful initial classifier learned from the quite limited labeled training data. In order to solve the above issues, in this paper we proposed a novel model of semi-supervised dictionary active learning (SSDAL), which aims to integrate semi-supervised learning and active learning to effectively use all the training data. In particular, two criterions based on estimated class possibility are designed to select the unlabeled data with confident class estimation for semi-supervised learning and the informative unlabeled data for active learning, respectively. Extensive experiments are conducted to show the superior performance of our method in classification applications, e.g., handwritten digit recognition, face recognition and large-scale image classification.

Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g., semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional modality. In this paper, we aim to investigate the feasibility of simultaneously leveraging abundant unlabeled data and well-established cross-modality data for annotation-efficient medical image segmentation. To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled target data and labeled source data (e.g., MR) by two teacher models. Specifically, the student model learns the knowledge of unlabeled target data from intra-domain teacher by encouraging prediction consistency, as well as the shape priors embedded in labeled source data from inter-domain teacher via knowledge distillation. Consequently, the student model can effectively exploit the information from all three data resources and comprehensively integrate them to achieve improved performance. We conduct extensive experiments on MM-WHS 2017 dataset and demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance, outperforming semi-supervised learning and domain adaptation methods with a large margin.

End-to-end automatic speech recognition (ASR) has become a popular alternative to traditional module-based systems, simplifying the model-building process with a single deep neural network architecture. However, the training of end-to-end ASR systems is generally data-hungry: a large amount of labeled data (speech-text pairs) is necessary to learn direct speech-to-text conversion effectively. To make the training less dependent on labeled data, pseudo-labeling, a semi-supervised learning approach, has been successfully introduced to end-to-end ASR, where a seed model is self-trained with pseudo-labels generated from unlabeled (speech-only) data. Here, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">momentum pseudo-labeling</i> (MPL), a simple yet effective strategy for semi-supervised ASR. MPL consists of a pair of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline</i> models that interact and learn from each other, inspired by the mean teacher method. The online model is trained to predict pseudo-labels generated on the fly by the offline model. The offline model maintains an exponential moving average of the online model parameters. The interaction between the two models allows better ASR training on unlabeled data by continuously improving the quality of pseudo-labels. We apply MPL to a connectionist temporal classification-based model and evaluate it on various semi-supervised scenarios with varying amounts of data or domain mismatch. The results demonstrate that MPL significantly improves the seed model by stabilizing the training on unlabeled data. Moreover, we present additional techniques, e.g., the use of Conformer and an external language model, to further enhance MPL, which leads to better performance than other semi-supervised methods based on pseudo-labeling.

Deep Semi-Supervised Learning (SSL) has shown very effective performance in recent years, such methods are typically under assumptions of a closed-world setting, where instances in the labeled and unlabeled data share the same class set. However, in real-world applications, samples with novel classes may be contained in the unlabeled data during the model deployment (open-set scenario), i.e. new types of scene images may occur in a self-driving system. In this paper, we advocate a two-stage semi-supervised learning approach CAPT, a framework for handling this realistic sce-nario based on a self-supervised pre-training step. The key idea is to introduce the embedding from pre-training into the SSL open-set classifier, so that the model can recognize the seen classes and cluster the instances from novel categories simultaneously. Our framework first pre-train a semantically meaningful representation of all samples from the labeled and unlabeled dataset. Next, CAPT applies the learned embedding as initialization to build a semisupervised classifier for clustering novel classes. We thoroughly evaluate our framework on large-scale image benchmarks CIFAR10, CIFAR100, obtaining state-of-the-art results.

Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Learning (SsCL), which combines the well-known contrastive loss in self-supervised learning with the cross entropy loss in semi-supervised learning, and jointly optimizes the two objectives in an end-to-end way. The highlight is that different from self-training based semi-supervised learning that conducts prediction and retraining over the same model weights, SsCL interchanges the predictions over the unlabeled data between the two branches, and thus formulates a co-calibration procedure, which we find is beneficial for better prediction and avoid being trapped in local minimum. Towards this goal, the contrastive loss branch models pairwise similarities among samples, using the pseudo labels generated from the cross entropy branch, and in turn calibrates the prediction distribution of the cross entropy branch with the contrastive similarity. We show that SsCL produces more discriminative representation and is beneficial to semi-supervised learning. Notably, on ImageNet with ResNet50 as the backbone, SsCL achieves 60.2% and 72.1% top-1 accuracy with 1% and 10% labeled samples, respectively, which significantly outperforms the baseline, and is better than previous semi-supervised and self-supervised methods.

Improving state-of-health estimation for lithium-ion batteries via unlabeled charging data

In this paper, the application of semi-supervised manifold learning techniques to the task of verifying hypothesized occurrences of spoken terms is investigated. These techniques are applied in a two stage spoken term detection framework where ASR lattices are first generated using a large vocabulary ASR system and hypothesized occurrences of spoken query terms in the lattices are verified in a second stage. The verification process is performed using a fixed dimensional feature representation derived from each hypothesized term occurrence. Two semi-supervised approaches namely, manifold regularized least squares (RLS) classification and spectral clustering, are investigated for distinguishing correct hypotheses from false alarms. It is shown that, exploiting unlabeled data in addition to labeled data using semi-supervised approaches, significantly improves the verification performance compared to the case where only the labeled data is used. This improvement in performance increases as the ratio of unlabeled to labeled data augments. It is also shown that, when training data is very limited, a comparable verification performance can be gained by exploiting only the acoustic similarity between the test samples using the spectral clustering approach.

Automatic Parts of Speech(POS) tagging is one of the most fundamental tasks for a language in Natural Language Processing(NLP), which acts as a feature for solving advanced NLP tasks. Named Entity Recognition(NER) is another essential task of NLP for information retrieval. Researchers could not find up to the mark solution yet on these two tasks for Bangla language compared to other languages, for instance, English, Ger-man. Moreover, many solutions heavily depend on handcrafted features that require strong linguistic expertise. As these two sequence labeling tasks are similar, In this work, two different datasets of POS tagging and NER were prepared, and different deep neural network approaches studied for solving these two tasks separately. All of the approaches were end to end and did not need any handcrafted feature like word suffixes or affixes, gazetteers, dictionary. This study came up with an end to end solution using deep neural network-based model consisting of Bi-directional Long short-term memory(BLSTM), Convolutional Neural Network(CNN) and Conditional Random Field(CRF). The proposed model trained on respected datasets achieved an accuracy of 93.86% on POS tagging and a strict f1 score of 0.6285 on NER on prepared datasets, respectively.

BackgroundUnderstanding the functional effects of non-coding variants is important as they are often associated with gene-expression alteration and disease development. Over the past few years, many computational tools have been developed to predict their functional impact. However, the intrinsic difficulty in dealing with the scarcity of data leads to the necessity to further improve the algorithms. In this work, we propose a novel method, employing a semi-supervised deep-learning model with pseudo labels, which takes advantage of learning from both experimentally annotated and unannotated data.ResultsWe prepared known functional non-coding variants with histone marks, DNA accessibility, and sequence context in GM12878, HepG2, and K562 cell lines. Applying our method to the dataset demonstrated its outstanding performance, compared with that of existing tools. Our results also indicated that the semi-supervised model with pseudo labels achieves higher predictive performance than the supervised model without pseudo labels. Interestingly, a model trained with the data in a certain cell line is unlikely to succeed in other cell lines, which implies the cell-type-specific nature of the non-coding variants. Remarkably, we found that DNA accessibility significantly contributes to the functional consequence of variants, which suggests the importance of open chromatin conformation prior to establishing the interaction of non-coding variants with gene regulation.ConclusionsThe semi-supervised deep learning model coupled with pseudo labeling has advantages in studying with limited datasets, which is not unusual in biology. Our study provides an effective approach in finding non-coding mutations potentially associated with various biological phenomena, including human diseases.

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data. The COUGHVID dataset enlisted expert physicians to annotate and diagnose the underlying diseases present in a limited number of recordings. However, this approach suffers from potential cough mislabeling, as well as disagreement between experts.Methods: In this work, we use a semi-supervised learning (SSL) approach – based on audio signal processing tools and interpretable machine learning models – to improve the labeling consistency of the COUGHVID dataset for 1) COVID-19 versus healthy cough sound classification 2) distinguishing wet from dry coughs, and 3) assessing cough severity. First, we leverage SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and label sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classifiers.Results: The consistency of the re-labeled COVID-19 and healthy data is demonstrated in that it exhibits a high degree of inter-class feature separability: 3x higher than that of the user-labeled data. Similarly, the SSL method increases this separability by 11.3x for cough type and 5.1x for severity classifications. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs in the 1-1.5 kHz range (p=1.2×10−64), which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a COVID-19 classifier, achieving an AUC of 0.797.Conclusions: We propose a SSL expert knowledge aggregation technique for the field of cough sound classification for the first time, and demonstrate how it can be used to combine the medical knowledge of multiple experts in an explainable fashion, thus providing abundant, consistent data for cough classification tasks.