Search Results (45)

This study introduces a multi-mechanism entity–relation extraction model designed to address persistent challenges in natural language processing, including syntactic complexity, long-range dependency modeling, and suboptimal utilization of contextual information. The proposed architecture integrates several complementary components. First, a pre-trained Chinese-RoBERTa-wwm-ext encoder with a whole-word masking strategy is employed to preserve lexical semantics and enhance contextual representations for multi-character Chinese text. Second, BiLSTM-based sequential modeling is incorporated to capture bidirectional contextual dependencies, facilitating the identification of distant entity relations. Third, the combination of multi-head attention and gated attention mechanisms enables the model to selectively emphasize salient semantic cues while suppressing irrelevant information. To further improve global prediction consistency, a Conditional Random Field (CRF) layer is applied at the output stage. Building upon this multi-mechanism framework, an adaptive dynamic network is introduced to enable input-dependent activation of feature modeling modules based on sentence-level semantic complexity. Rather than enforcing a fixed computation pipeline, the proposed mechanism supports flexible and context-aware feature interaction, allowing the model to better accommodate heterogeneous sentence structures. Experimental results on benchmark datasets demonstrate that the proposed approach achieves strong extraction performance and improved robustness, making it a flexible solution for downstream applications such as knowledge graph construction and semantic information retrieval. Full article

With the advancement of educational informatization, vast amounts of Chinese text are generated across online platforms and digital textbooks. Effectively classifying such text is essential for intelligent education systems. This study conducts a systematic comparative evaluation of three Transformer-based models—TinyBERT-4L, BERT-base-Chinese, and RoBERTa-wwm-ext—for Chinese educational text classification. Using a balanced four-category subset of the THUCNews corpus (Education, Technology, Finance, and Stock), the research investigates the trade-off between classification effectiveness and computational efficiency under a unified experimental framework. The experimental results show that RoBERTa-wwm-ext achieves the highest effectiveness (93.12% Accuracy, 93.08% weighted F1), validating the benefits of whole-word masking and extended pre-training. BERT-base-Chinese maintains a balanced performance (91.74% Accuracy, 91.66% F1) with moderate computational demand. These findings reveal a clear symmetry–asymmetry dynamic: structural symmetry arises from the shared Transformer encoder and identical fine-tuning setup, while asymmetry emerges from differences in model scale and pre-training strategy. This interplay leads to distinct accuracy–latency trade-offs, providing practical guidance for deploying pre-trained language models in resource-constrained intelligent education systems. Full article

High-voltage switchgear is an important component of the power system, and its operation safety will directly affect the reliability of the power supply of the power system. At present, the operation and maintenance decision-making of the switchgear mainly relies on manual work, which has problems such as low efficiency and poor reliability of judgment results. Therefore, this paper proposes an intelligent operation and maintenance auxiliary method for high-voltage switchgear based on the combination of the Ripplenet algorithm and knowledge graph, which ensures high efficiency while improving the reliability of the results. Among them, the knowledge graph is mainly based on the Bidirectional Encoder Representations from Transformers-Whole Word Masking (BERT-wwm) algorithm, and it is constructed in a bottom-up and top-down manner. It consists of 240 nodes and 960 relationships. Based on this knowledge graph, the intelligent operation and maintenance auxiliary method of high-voltage switchgear based on Ripplenet is studied. Based on textual information such as on-site information and fault reports, the judgment reasoning of the fault type of the high-voltage switchgear and recommendations for operation and maintenance solutions are realized. The diagnostic accuracy of this method for high-voltage switchgear faults can reach 95.96%. Full article

A student’s active thinking state directly affects their learning experience in the classroom. To help teachers understand students’ active thinking states in real-time, this study aims to construct a model which characterizes their active thinking states. The main research objectives are as follows: (1) to achieve accurate classification of the cognitive levels of in-class exercises; (2) to effectively quantify the active thinking state of students through analyzing the correlation between student cognitive levels and exercise cognitive levels. The research methods used in this study to achieve these objectives are as follows: First, LSTM and Chinese-RoBERTa-wwm models are integrated to extract sequential and semantic information from plain text while TBCC is used to extract the semantic features of code text, allowing for comprehensive determination of the cognitive level of exercises. Second, a cognitive diagnosis model—namely, the QRCDM—is adopted to evaluate students’ real-time cognitive levels with respect to knowledge points. Finally, the cognitive levels of exercises and students are input into a self-attention mechanism network, their correlation is analyzed, and the thinking activity state is generated as a state representation. The proposed text classification model outperforms baseline models regarding ACC, micro-F1, and macro-F1 scores on two sets of exercise datasets in Chinese containing mixed code texts, with the highest ACC, micro-F1, and macro-F1 values reaching 0.7004, 0.6941, and 0.6912, respectively. This proves the proposed model’s effectiveness in classifying the cognitive level of exercises. The accuracy of the thinking activity state characterization model reaches 61.54%. In particular, this is higher than the random baseline, thus verifying the model’s feasibility. Full article

With the increasing application of manufactured sand, as one of the uncertain factors affecting the properties and performance of ready-mixed concrete proportioning with commonly used manufactured sand, residual flocculants in water-washed manufactured sand (WWMS) have received increased attention. Under certain prerequisites, a rapid detecting method for residual flocculants in WWMS was presented based on the pre-calibrated relationship between the Stormer viscosity of cement paste and the concentration of flocculants. Multi-dimensional and multi-factorial experiments were performed on cement paste, mortar and concrete orderly to explore the effects of flocculant content on the rheological (workability) and mechanical properties (compressive strength) of concrete. The results showed a good quantitative relationship between the Stormer viscosity and the flocculant content, and its mathematical formula depended on the type, molecular weight and content range of the flocculant. The residual flocculant contents in WWMS not only affected the workability of fresh concrete, but also the strength of hardened concrete to some extent. Full article

This study investigated the dynamic resolution of lexical–semantic ambiguity during sentence comprehension, focusing on how uncertainty evolves as contextual information accumulates. Using time-resolved eye-tracking and a novel entropy-based measure derived from group-level semantic choice distributions, we quantified semantic uncertainty at a fine-grained temporal resolution for ambiguous words. By parametrically manipulating the semantic bias strength of the sentence context, we examined how context guides disambiguation over time. The results showed that semantic uncertainty declined gradually over temporal segments and dropped sharply following the onset of ambiguous words, reflecting both incremental integration and syntactic anchoring. A stronger contextual bias led to faster reductions in uncertainty, with effects following a near-linear trend. These findings support dynamic semantic processing models that assume continuous, context-sensitive convergence toward intended meanings. In contrast, a pretrained Chinese BERT model (RoBERTa-wwm-ext) showed similar overall trends in uncertainty reduction but lacked sensitivity to contextual bias. This discrepancy suggests that, while language models can approximate human-level disambiguation broadly, they fail to capture fine-grained semantic modulation driven by context. These findings provide a novel empirical characterization of disambiguation dynamics and offer a new methodological approach to capturing real-time semantic uncertainty. The observed divergence between human and model performance may inform future improvements to language models and contributes to our understanding of possible architectural differences between human and artificial semantic systems. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

The quantification of emotional value and accurate prediction of purchase intention has emerged as a critical interdisciplinary challenge in the evolving emotional economy. Focusing on Generation Z (born 1995–2009), this study proposes a hybrid algorithmic framework integrating text-based sentiment computation, feature selection, and random forest modeling to forecast purchase intention for therapeutic toys and interpret its underlying drivers. First, 856 customer reviews were scraped from Jellycat’s official website and subjected to polarity classification using a fine-tuned RoBERTa-wwm-ext model (F1 = 0.92), with generated sentiment scores and high-frequency keywords mapped as interpretable features. Next, Boruta–SHAP feature selection was applied to 35 structured variables from 336 survey records, retaining 17 significant predictors. The core module employed a RF (random forest) model to estimate continuous “purchase intention” scores, achieving R² = 0.83 and MSE = 0.14 under 10-fold cross-validation. To enhance interpretability, RF model was also utilized to evaluate feature importance, quantifying each feature’s contribution to the model outputs, revealing Social Ostracism (β = 0.307) and Task Overload (β = 0.207) as dominant predictors. Finally, k-means clustering with gap statistics segmented consumers based on emotional relevance, value rationality, and interest level, with model performance compared across clusters. Experimental results demonstrate that our integrated predictive model achieves a balance between forecasting accuracy and decision interpretability in emotional value computation, offering actionable insights for targeted product development and precision marketing in the therapeutic goods sector. Full article

In the context of China’s accelerating maritime judicial digitization, automatic summarization of lengthy and terminology-rich judgment documents has become a critical need for improving legal efficiency. Focusing on the task of automatic summarization for Chinese maritime judgment documents, we propose HybridSumm, an “extraction–abstraction” hybrid summarization framework that integrates a maritime judgment lexicon to address the unique characteristics of maritime legal texts, including their extended length and dense domain-specific terminology. First, we construct a specialized maritime judgment lexicon to enhance the accuracy of legal term identification, specifically targeting the complexity of maritime terminology. Second, for long-text processing, we design an extractive summarization model that integrates the RoBERTa-wwm-ext pre-trained model with dilated convolutional networks and residual mechanisms. It can efficiently identify key sentences by capturing both local semantic features and global contextual relationships in lengthy judgments. Finally, the abstraction stage employs a Nezha-UniLM encoder–decoder architecture, augmented with a pointer–generator network (for out-of-vocabulary term handling) and a coverage mechanism (to reduce redundancy), ensuring that summaries are logically coherent and legally standardized. Experimental results show that HybridSumm’s lexicon-guided two-stage framework significantly enhances the standardization of legal terminology and semantic coherence in long-text summaries, validating its practical value in advancing judicial intelligence development. Full article

Power equipment maintenance work orders are vital in power equipment management because they contain detailed information such as equipment specifications, defect reports, and specific maintenance activities. However, due to limited research into automated information extraction, valuable operational and maintenance data remain underutilized. A key challenge is recognizing unstructured Chinese maintenance texts filled with specialized and abbreviated terms unique to the power sector. Existing named entity recognition (NER) solutions often fail to effectively manage these complexities. To tackle this, this paper proposes a NER model tailored to power equipment maintenance work orders. First, a dataset called power equipment maintenance work orders (PE-MWO) is constructed, which covers seven entity categories. Next, a novel position- and similarity-aware attention module is proposed, where an innovative position embedding method and attention score calculation are designed to improve the model’s contextual understanding while keeping computational costs low. Further, with this module as the main body, combined with the BERT-wwm-ext and conditional random field (CRF) modules, an efficient NER model is jointly constructed. Finally, validated on the PE-MWO and five public datasets, our model shows high accuracy in recognizing power sector entities, outperforming comparative models on public datasets. Full article

In the domain of natural language processing (NLP), a primary challenge pertains to the process of Chinese tokenization, which remains challenging due to the lack of explicit word boundaries in written Chinese. The existing tokenization methods often treat each Chinese character as an indivisible unit, neglecting the finer semantic features embedded in the characters, such as radicals. To tackle this issue, we propose a novel token representation method that integrates radical-based features into the process. The proposed method extends the vocabulary to include both radicals and original character tokens, enabling a more granular understanding of Chinese text. We also conduct experiments on seven datasets covering multiple Chinese natural language processing tasks. The results show that our method significantly improves model performance on downstream tasks. Specifically, the accuracy of BERT on the BQ Croups dataset was enhanced to 86.95%, showing an improvement of 1.65% over the baseline. Additionally, the BERT-wwm performance demonstrated a 1.28% enhancement, suggesting that the incorporation of fine-grained radical features offers a more efficacious solution for Chinese tokenization and paves the way for future research in Chinese text processing. Full article

To enhance the efficiency of reusing massive unstructured operation and maintenance (O&M) data generated during routine railway maintenance inspections, this paper proposes a Named Entity Recognition (NER) method that integrates multi-granularity semantics and a Multi-Scale Retention (MSR) mechanism. The proposed approach effectively transforms expert knowledge extracted from manually processed fault data into structured triplet information, enabling the in-depth mining of track circuit O&M text data. Given the specific characteristics of railway domain texts, which include a high prevalence of technical terms, ambiguous entity boundaries, and complex semantics, we first construct a domain-specific lexicon stored in a Trie tree structure. A lexicon adapter is then introduced to incorporate these terms as external knowledge into the base encoding process of RoBERTa-wwm-ext, forming the lexicon-enhanced LE-RoBERTa-wwm model. Subsequently, a hidden feature extractor captures semantic representations from all 12 output layers of LE-RoBERTa-wwm, performing weighted fusion to fully leverage multi-granularity semantic information across encoding layers. Furthermore, in the downstream processing stage, two computational paradigms are designed based on the MSR mechanism and the Regularized Dropout (R-Drop) mechanism, enabling low-cost inference and efficient parallel training. Comparative experiments conducted on the public Resume and Weibo datasets demonstrate that the model achieves F1 scores of 96.75% and 72.06%, respectively. Additional experiments on a track circuit dataset further validate the model’s superior recognition performance and generalization capability. Full article

Typhoons cause significant losses and pose substantial threats every year, with an increasing trend observed in recent years. This study evaluates significant wave height (SWH) hindcasts for typhoons affecting Taiwan using optimized wind field configurations within the SCHISM-WWM-III coupled model. To enhance typhoon-induced SWH simulations, the blended wind field integrates ERA5 reanalysis wind data with the modified Rankine vortex wind model. Key parameters, including the parametric wind field start time, best track data, and the radius of maximum wind speed, were carefully selected based on analyses of typhoons Meranti and Megi in 2016. Validation metrics such as the skill core, HH indicator, maximum SWH difference, and peak time difference of the SWH indicate that the optimized setup improves the accuracy of simulation. The findings highlight the effectiveness of the adjusted blended wind field, the high-resolution best track data provided by Taiwan, and the maximum wind speed radius in significantly enhancing the accuracy of typhoon wave modeling for the waters surrounding Taiwan. Full article

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively. Full article

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text. Full article