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Aspect Sentiment Quad Prediction is a research topic of paramount significance and complexity within the Aspect-Based Sentiment Analysis task. Leveraging the generative paradigm of the T5 model, we achieve end-to-end extraction of aspect sentiment elements by paraphrasing the original text into sentences predefined by templates. Current research predominantly confines templates to single sentences or directly concatenates sentiment elements using a few symbols, limiting the model’s reasoning opportunities. In this work, we introduce a Self-Inference Template (SIT) to guide the model in thoughtful reasoning, facilitating a step-by-step inference generation process. This approach enables the model to more accurately identify aspect sentiment elements and their interdependencies. Experimental results demonstrate a significant improvement in quadruplet prediction performance under constant time costs, effectively mitigating overfitting issues caused by limited data volume to some extent.

This paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning. The framework, dubbed PrdSimCSE, uses Prd to create both positive and negative sample pairs. By appending positive and negative prefixes to a sentence, the basis for contrastive learning is formed, outperforming the baseline unsupervised SimCSE. PrdSimCSE is positioned within a probabilistic framework that expands the semantic similarity event space and generates superior negative samples, contributing to more accurate semantic similarity estimations. The model’s efficacy is validated on standard semantic similarity tasks, showing a notable improvement over that of existing unsupervised models, specifically a 1.08% enhancement in performance on BERTbase. Through detailed experiments, the effectiveness of positive and negative prefixes in data augmentation and their impact on the learning model are explored, and the broader implications of prefix data augmentation are discussed for unsupervised sentence embedding learning.

Support vector machine (SVM) is one of the most powerful technologies of machine learning, which has been widely concerned because of its remarkable performance. However, when dealing with the classification problem of large-scale datasets, the high complexity of SVM model leads to low efficiency and become impractical. Due to the sparsity of SVM in the sample space, this paper presents a new parallel data geometry analysis (PDGA) algorithm to reduce the training set of SVM, which helps to improve the efficiency of SVM training. The PDGA introduce Mahalanobis distance to measure the distance from each sample to its centroid. And based on this, proposes a method that can identify non support vectors and outliers at the same time to help remove redundant data. When the training set is further reduced, cosine angle distance analysis method is proposed to determine whether the samples are redundant data, ensure that the valuable data are not removed. Different from the previous data geometry analysis methods, the PDGA algorithm is implemented in parallel, which greatly saving the computational cost. Experimental results on artificial dataset and 6 real datasets show that the algorithm can adapt to different sample distributions. Which significantly reduce the training time and memory requirements without sacrificing the classification accuracy, and its performance is obviously better than the other five competitive algorithms.

In the existing research on time-series event prediction (TSEP) methods, most of the work is focused on improving the algorithm for classifying subsequence sets (sets composed of multiple adjacent subsequences). However, these prediction methods ignore the timing dependence between the subsequence sets, nor do they capture the mutual transition relationship between events, the prediction effect on a small sample data set is very poor. Meanwhile, the sequence labeling problem is one of the common problems in natural language processing and image segmentation. To solve this problem, this paper proposed a new framework for time-series event prediction, which transforms the event prediction problem into a labeling problem, to better capture the timing relationship between the subsequence sets. Specifically, the framework used a sequence clustering algorithm for the first time to identify representative patterns in the time series, then represented the set of subsequences as a weighted combination of patterns, and used the eXtreme gradient boosting algorithm (XGBoost) for feature selection. After that, the selected pattern feature was used as the input of the long-term short-term memory model (LSTM) to obtain the preliminary prediction value. Furthermore, the fully-linked conditional random field (CRF) was used to smooth and refine the preliminary prediction value to obtain the final prediction result. Finally, the experimental results of event prediction on five real data sets show that the CX-LC method has a certain improvement in prediction accuracy compared with the other six models.

The long text classification task has become a hot research topic in the field of text classification due to its long length and redundant information. At present, the common processing methods for long text data, such as the truncation method and pooling method, are prone to the problem of too many sentences or loss of contextual semantic information. To deal with these issues, we present LTTA-LE (Long Text Truncation Algorithm Based on Label Embedding in Text Classification), which consists of three key steps. Firstly, we build a pretraining prefix template and a label word mapping prefix template to obtain the label word embedding, and we realize the joint training of long text and label words. Secondly, we calculate the cosine similarity between the label word embedding and the long text embedding, and we filter the redundant information of the long text to reduce the text length. Finally, a three-stage model training architecture is introduced to effectively improve the classification performance and generalization ability of the model. We conduct comparative experiments on three public long text datasets, and the results show that LTTA-LE has an average F1 improvement of 1.0518% over other algorithms, which proves that our method can achieve satisfactory performance.

Text classification is the critical content of machine learning, and it is widely applied in information filtering, sentimental analysis, and text review. It is very important to improve the accuracy of classification results, and this is also the main research purpose of researchers in this field in recent years. Feature selection plays an important role in text classification, which has the functions of eliminating irrelevant features, reducing dimensionality, and improving classification accuracy. So, this paper studies the CHI feature selection algorithm, and the main work and innovations are as follows: firstly, this paper analyzed the CHI algorithm’s flaws, determined that the introduction of new parameters will be the improvement direction of the CHI algorithm, and thus proposed a new algorithm based on variance and coefficient of variation. Secondly, experiment to verify the effectiveness of the new algorithm. In terms of language, the experiment in this paper includes two text classification systems, which were Chinese and English. In terms of classifiers, two classifier algorithms were used, which included the KNN classifier and the Naive Bayes classifier. In terms of data types, two distribution types of data were used: balanced datasets and unbalanced datasets. Finally, experiment and result analysis. This paper has conducted 3 comparative experiments and analyzed the results of each experiment. The experimental results obtained are all significantly improved compared to the results before the improvement.

Polyvinyl alcohol- (PVA-) based aerogels have attracted widespread attention owing to their low cost, eco-friendliness, and low density. However, the applications of PVA-based aerogels are limited by their flammability. In this study, a flame retardant, ammonium polyphosphate (APP), and a biopolymer, chitosan (CS), were added to polyvinyl alcohol (PVA), and the polymer was further crosslinked using boric acid (H3BO3). In the PVA aerogels, the negatively charged APP and positively charged CS formed a polyelectrolyte complex (PEC) through ionic interaction. Cone calorimetry and vertical burning tests (UL-94) indicated that the PVA composite aerogels have excellent flame retardancy; they could decrease the heat release rate, total heat release rate, and carbon dioxide (CO2) generation. Both PVA/H3BO3 and APP-CS in the composite aerogel could be burned to carbon, and the foamed char layer could act together to impart the PVA composite aerogels with good flame retardancy. Further, the decrease in the temperature at the backside of the aerogels with increasing APP-CS content, as determined by the flame-spraying experiment, indicated that the PVA-based aerogels with APP-CS can also serve as thermal insulation materials. This work provides an effective and promising method for the preparation of PVA-based aerogels with good flame retardancy and thermal insulation property for construction materials.

Water-fracs is a key technology to increase yield and injection in low permeability oil and gas field, while it has a certain limitation. The application of the water-fracs mainly depends on the flow conductivity provided by self-supporting fracture in reservoir. The steering, slipping and closing processes of self-supporting fracture are simulated with the finite element simulation method under the consideration of the rock heterogeneity, deviation stress, the angle between fracture initial direction and maximum principal stress direction, and the irregular curve fracture. The sensitivity factors and effect rules of the angle of the direction of crack growth and slipping distance are obtained,and the formation mechanism of fracture conductivity is revealed. The preparation method of the wall combination of the self-supporting fracture indoor and the test method of the flow conductivity under different closure pressure are established. The result provides a theoretical foundation for the water-fracs increasing production mechanism and the appropriate reservoir conditions. [ABSTRACT FROM AUTHOR]