Extraction Framework Research Articles

A Comprehensive but Effective Share-Dependent Bidirectional Framework for Relational Triple Extraction

A Comprehensive but Effective Share-Dependent Bidirectional Framework for Relational Triple Extraction

Strong size sieving effect in a rigid oxalate-based metal-organic framework for selective lithium extraction.

An oxalate-based metal-organic framework Eu-C2O4 was synthesized at gram-scale and studied as a selective adsorbent for Li+ ions, and it exhibited high Li+/Na+ selectivity in aqueous solution. A detailed mechanism study revealed that the key was the well-matched chelating sites of the framework for Li+ ion extraction.

DeepKP: A Robust and Accurate Framework for Weld Seam Keypoint Extraction in Welding Robots

DeepKP: A Robust and Accurate Framework for Weld Seam Keypoint Extraction in Welding Robots

CMBEE: A constraint-based multi-task learning framework for biomedical event extraction

Objective:Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction. Material and Methods:We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events. Results:Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13). Conclusions:The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.

Construction of cationic covalent organic framework for efficient gold extraction.

In this work, we report a rare ionic covalent organic framework (named as ECUT-iCOF-5) synthesized by the Knoevenagel reaction using an ionic liquid monomer. ECUT-iCOF-5 is found to show a promising potential for gold extraction with a high extraction ability towards gold, including a high extraction capacity (3260 mg g-1), good selectivity over both cations and anions, and good recycle use (at least six times).

HCL: A Hierarchical Contrastive Learning Framework for Zero-Shot Relation Extraction.

Zero-shot relation extraction (ZSRE) is shown to become more significant in the current information extraction system, which aims at predicting relation classes that lack annotations or have just never appeared during training. Previous works focus on projecting sentences with their corresponding relation descriptions to an intermediate semantic space and searching the nearest semantic for predicting unseen classes. Though these methods can achieve sound performance, they only obtain inferior semantic information via a trivial distance metric and neglect the interaction in the instance representations. We are thus motivated to tackle these issues and propose a hierarchical contrastive learning (HCL) framework for ZSRE including projection-level and instance-level modules. Specifically, the projection-level component replaces the distance score function by contrastive loss to connect the input sentence with the relation semantic space. And the instance-level component integrates the external knowledge from sentence entities to establish new contrastive pairs for efficiently learning representations from mutual information. The experimental results on three well-known datasets demonstrate that our model surpasses the existing SOTA by at most 18.97% improvement on the F1 score when unseen classes are 15 . Moreover, our model can achieve more competitive performance alone with the increasing number of unseen classes.

A Sparse Sharing Multitask Framework for Building Footprint Extraction From Remote Sensing Imagery Following the Dual Lottery Ticket Hypothesis

A Sparse Sharing Multitask Framework for Building Footprint Extraction From Remote Sensing Imagery Following the Dual Lottery Ticket Hypothesis

A Road-detail Preserving Framework for Urban Road Extraction from VHR Remote Sensing Imagery

A Road-detail Preserving Framework for Urban Road Extraction from VHR Remote Sensing Imagery

Fatigue state recognition system for miners based on a multi-modal feature extraction and fusion framework

Fatigue state recognition system for miners based on a multi-modal feature extraction and fusion framework

Toward Transparent AI for Neurological Disorders: A Feature Extraction and Relevance Analysis Framework

Toward Transparent AI for Neurological Disorders: A Feature Extraction and Relevance Analysis Framework

A Mutual-Attention Guided Feature Extraction and Adaptative Decision Fusion Framework for Fine-Grained Dual-Band Radar Target Classification

A Mutual-Attention Guided Feature Extraction and Adaptative Decision Fusion Framework for Fine-Grained Dual-Band Radar Target Classification

An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data.

Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately classifying brain tumors based on medical imaging data such as MRI scans. These techniques aid in detecting and planning treatment early, improving patient outcomes. However, medical image datasets are frequently affected by a significant class imbalance, especially when benign tumors outnumber malignant tumors in number. This study presents an explainable ensemble-based pipeline for brain tumor classification that integrates a Dual-GAN mechanism with feature extraction techniques, specifically designed for highly imbalanced data. This Dual-GAN mechanism facilitates the generation of synthetic minority class samples, addressing the class imbalance issue without compromising the original quality of the data. Additionally, the integration of different feature extraction methods facilitates capturing precise and informative features. This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. This study focuses on achieving high classification accuracy while prioritizing stable performance. By incorporating Grad-CAM, it enhances the transparency and interpretability of the overall classification process. This research identifies the most relevant and contributing parts of the input images toward accurate outcomes enhancing the reliability of the proposed pipeline. The significantly improved Precision, Sensitivity and F1-Score demonstrate the effectiveness of the proposed mechanism in handling class imbalance and improving the overall accuracy. Furthermore, the integration of explainability enhances the transparency of the classification process to establish a reliable model for brain tumor classification, encouraging their adoption in clinical practice promoting trust in decision-making processes.

A large-scale extraction framework for mapping urban in-formal settlements using remote sensing and semantic segmentation

Urban informal settlements (UISs) are densely populated and poorly developed residential areas in urban areas. The mapping of UISs using remote sensing is crucial for urban planning and management. However, the large-scale extraction of UISs is impeded by the labor-intensive task of collecting numerous training samples and the lack of automatic and effective city partition. To overcome these challenges, we proposed a large-scale extraction framework for UISs based on semantic segmentation of high-resolution remote sensing images. Utilizing Deeplab V3 Plus as the foundational extraction model, the proposed framework introduces fast sample collection based on GLCM features. Besides, an automatic city partition approach combined with clustering and fine-tuning was proposed to enhance the performance on extracting a specific category of UISs. The results of the case study conducted in 36 major Chinese cities show that the proposed framework achieved good performance, with an overall F1 score of 85.76%. Furthermore, comparative assessments were performed to demonstrate the effectiveness of automatic city partition. The proposed framework offers a practical approach for the large-scale extraction of UISs, which holds great significance for sustainable development, poverty estimation, infrastructure construction, and urban planning.

A novel framework for tongue feature extraction framework based on sublingual vein segmentation.

The features of the sublingual veins, including swelling, varicose patterns, and cyanosis, are pivotal in differentiating symptoms and selecting treatments in Traditional Chinese Medicine (TCM) tongue diagnosis. These features serve as a crucial reflection of the human blood circulation status. Nevertheless, the automatic and precise extraction of sublingual vein features remains a formidable challenge, constrained by the scarcity of datasets for sublingual images and the interference of noise from non-tongue and non-sublingual vein elements. In this paper, we present an innovative tongue feature extraction method that relies on focusing specifically on segmenting the sublingual vein rather than the entire tongue base. To achieve this, we have developed a sublingual vein segmentation framework utilizing a Polyp-PVT network, effectively eliminating noise from the surrounding regions of the sublingual vein. Furthermore, we pioneer the utilization of a transformer-based approach, such as the Swin-Transformer network, to extract sublingual vein features, leveraging the remarkable capabilities of transformer networks. To complement our methodology, we have constructed a comprehensive dataset of sublingual vein images, facilitating the segmentation and classification of sublingual veins. Experimental results have demonstrated that our tongue feature extraction method, coupled with sublingual vein segmentation, significantly outperforms existing tongue feature extraction techniques.

OpenVTER: An Open Vehicle Trajectory Extraction Framework Based on Rotated Bounding Boxes

OpenVTER: An Open Vehicle Trajectory Extraction Framework Based on Rotated Bounding Boxes

Cost-Effective Knowledge Extraction Framework for Low-Resource Environments

Cost-Effective Knowledge Extraction Framework for Low-Resource Environments

The Use of Multi-Feature Fusion in the Evaluation of Emotional Expressions in Spoken English

Abstract Sentiment analysis is one of the important applications in the field of natural language processing. With the development of science and technology, sentiment analysis is developing in the direction of multi-feature fusion, and multi-feature fusion plays an important value in application in English spoken emotional expression. In this paper, we design a method for extracting multi-features based on multi-networks and propose a sentiment analysis model, ECISA-MFF model, on the basis of a multi-feature extraction framework and feature fusion scheme to solve the problem of data non-alignment and modal noise, and then further classify the sentiments and optimize the model. The article concludes with a comparison of the relevant performance of the models as well as a case study, and it is found that the F-macro value of the model proposed in this paper rises by 22.2% when compared with the BiLSTM+att+C model, which is significantly effective. After applying this paper’s method for emotion recognition, it was found that the average recognition rate of six emotions (happiness, anger, fear, surprise, sadness, and disgust) increased to 65.52%.

Multi-Time-Scale Shapelet-Based Feature Extraction for Non-Intrusive Load Monitoring

The performance of non-intrusive load monitoring (NILM) system is closely related to the uniqueness and comprehensiveness of the load features extracted from the equipment. However, existing feature extraction methods for NILM are difficult to extract interpretable intuitive features representing the uniqueness of load, and lack of comprehensive attention to the features on different time scales. Therefore, this paper proposes a multi-time-scale shapelet based feature extraction framework. First, shapelet, the most unique segment of a sequence, which can distinguish it from other sequences is introduced as a feature and multi-time-scale current shapelets are proposed to extract comprehensive characteristics of electric appliances and offer intuitive interpretability. Meanwhile, a shapelet transform is constructed, so that the essence of shapelet based NILM is transformed from sequence matching to a general classification problem. Next, an ensemble bagging classifier is proposed on multi-time scales to effectively balance the classification errors on each time scale and enhance the performance of NILM system. In addition, this framework can support small sample training to facilitate rapid deployment of NILM applications at the initial practical stage. The results show that this framework can effectively focus on the uniqueness of load on different time scales, provide interpretability for NILM, and achieve good classification effects.

Glacial Lake Extraction Framework Based on Coupling of GEE and Historical Glacial Lake Position

Glacial Lake Extraction Framework Based on Coupling of GEE and Historical Glacial Lake Position

Hybrid Approach To Unsupervised Keyphrase Extraction

The exponential growth of textual data poses a monumental challenge for extracting meaningful knowledge. Manually identifying descriptive keywords or keyphrases for each document is infeasible given the massive daily generated text. Automatic keyphrase extraction is, therefore, essential. However, current techniques struggle with learning the most salient semantic features from lengthy documents. This hybrid keyphrase extraction framework uniquely combines the complementary strengths of graph-based and textual feature methods. Our approach demonstrates improved performance over relying solely on statistical or graphical. Graph-based systems leverage word co- occurrence networks to score importance. Textual methods extract keyphrases using linguistic properties. Together, these complementary techniques overcome the limitations of relying on any strategy. The hybrid approach is evaluated on standard SemEval 2017 Task 10 and SemEval 2010 Task 5 benchmark datasets for scientific paper keyphrase extraction. Performance is quantified using the F1 score relative to human-annotated ground truth keyphrase. Results will quantify effectiveness on long documents with thousands of terms where only a few keywords represent salient concepts. Results show our technique effectively identifies the most salient semantic keywords, overcoming limitations of current techniques that struggle to mix features of graphical or statistical methods. Our experiments demonstrate that the proposed hybrid approach achieves superior F1 scores compared to current state-of-the-art methods on benchmark datasets. These results validate that synergistically combining graph and textual features enables more accurate keyphrase extraction, especially for long documents laden with extraneous terms.