Search Results (99)

Natural disasters like wildfires pose significant threats to communities, which necessitates timely and effective disaster response strategies. While Aspect-based Sentiment Analysis (ABSA) has been widely used to extract sentiment-related information at the sub-sentence level, the corresponding field of Aspect-based Emotion Analysis (ABEA) remains underexplored due to dataset limitations and the increased complexity of emotion classification. In this study, we used EmoGRACE, a fine-tuned BERT-based model for ABEA, which we applied to georeferenced tweets of the 2020 California wildfires. The results for this case study reveal distinct spatio-temporal emotion patterns for wildfire-related aspect terms, with fear and sadness increasing near wildfire perimeters. This study demonstrates the feasibility of tracking emotion dynamics across disaster-affected regions and highlights the potential of ABEA in real-time disaster monitoring. The results suggest that ABEA can provide a nuanced understanding of public sentiment during crises for policymakers. Full article

To address issues commonly observed during the inference phase of large language models—such as inconsistent labels, formatting errors, or semantic deviations—a series of targeted strategies has been proposed. First, a relation label refinement strategy based on semantic similarity and syntactic structure has been designed to calibrate the model’s outputs, thereby improving the accuracy and consistency of label prediction. Second, to meet the contextual modeling needs of different types of instance bags, a multi-level contextual augmentation strategy has been constructed. For multi-sentence instance bags, a graph-based retrieval enhancement mechanism is introduced, which integrates intra-bag entity co-occurrence networks with document-level sentence association graphs to strengthen the model’s understanding of cross-sentence semantic relations. For single-sentence instance bags, a semantic expansion strategy based on term frequency-inverse document frequency is employed to retrieve similar sentences. This enriches the training context under the premise of semantic consistency, alleviating the problem of insufficient contextual information. Notably, the proposed multi-granularity framework captures semantic symmetry between entities and relations across different levels of context, which is crucial for accurate and balanced relation understanding. The proposed methodology offers practical advancements for semantic analysis applications, particularly in knowledge graph development. Full article

This study investigates eye movement patterns as indicators of text complexity in Arabic, focusing on the comparative analysis of Classical Arabic (CA) and Modern Standard Arabic (MSA) text. Using the AraEyebility corpus, which contains eye-tracking data from readers of both CA and MSA text, we examined differences in fixation patterns, regression rates, and overall reading behavior between these two forms of Arabic. Our analyses revealed significant differences in eye movement metrics between CA and MSA text, with CA text consistently eliciting more fixations, longer fixation durations, and more frequent revisits. Multivariate analysis confirmed that language type has a significant combined effect on eye movement patterns. Additionally, we identified different relationships between text features and eye movements for CA versus MSA text, with sentence-level features emerging as significant predictors across both language types. Notably, we observed an interaction between language type and readability level, with readers showing less sensitivity to readability variations in CA text compared to MSA text. These findings contribute to our understanding of how historical language evolution affects reading behavior and have practical implications for Arabic language education, publishing, and assessment. The study demonstrates the value of eye movement analysis for understanding text complexity in Arabic and highlights the importance of considering language-specific features when studying reading processes. Full article

Aspect-Based Sentiment Analysis (ABSA) has gained significant popularity in recent years, which emphasizes the aspect-level sentiment representation of sentences. Current methods for ABSA often use pre-trained models and graph convolution to represent word dependencies. However, they struggle with long-range dependency issues in lengthy texts, resulting in averaging and loss of contextual semantic information. In this paper, we explore how richer semantic relationships can be encoded more efficiently. Inspired by quantum theory, we construct superposition states from text sequences and utilize them with quantum measurements to explicitly capture complex semantic relationships within word sequences. Specifically, we propose an attention-based semantic dependency fusion method for ABSA, which employs a quantum embedding module to create a superposition state of real-valued word sequence features in a complex-valued Hilbert space. This approach yields a word sequence density matrix representation that enhances the handling of long-range dependencies. Furthermore, we introduce a quantum cross-attention mechanism to integrate sequence features with dependency relationships between specific word pairs, aiming to capture the associations between particular aspects and comments more comprehensively. Our experiments on the SemEval-2014 and Twitter datasets demonstrate the effectiveness of the quantum-inspired attention-based semantic dependency fusion model for the ABSA task. Full article

Semantic text chunking refers to segmenting text into coherently semantic chunks, i.e., into sets of statements that are semantically related. Semantic chunking is an essential pre-processing step in various NLP tasks e.g., document summarization, sentiment analysis and question answering. In this paper, we propose a hybrid chunking; two-steps semantic text chunking method that combines the effectiveness of unsupervised semantic text chunking based on the similarities between sentences embeddings and the pre-trained language models (PLMs) especially BERT by fine-tuning the BERT on semantic textual similarity task (STS) to provide a flexible and effective semantic text chunking. We evaluated the proposed method in English and Arabic. To the best of our knowledge, there is an absence of an Arabic dataset created to assess semantic text chunking at this level. Therefore, we created an AraWiki50k to evaluate our proposed text chunking method inspired by an existing English dataset. Our experiments showed that exploiting the fine-tuned pre-trained BERT on STS enhances results over unsupervised semantic chunking by an average of 7.4 in the PK metric and by an average of 11.19 in the WindowDiff metric on four English evaluation datasets, and 0.12 in the PK and 2.29 in the WindowDiff for the Arabic dataset. Full article

With the advancement of information technology, artificial intelligence (AI) has demonstrated significant potential in clinical prediction, helping to improve the level of intelligent medical care. Current clinical practice primarily relies on patients’ time series data and clinical notes to predict health status and makes predictions by simply concatenating cross-modal features. However, they not only ignore the inherent correlation between cross-modal features, but also fail to analyze the collaborative representation of multi-granularity features from diverse perspectives. To address these challenges, we propose a deep dynamic memory-driven cross-modal feature representation network for clinical outcome prediction. Specifically, we use a Bi-directional Gated Recurrent Unit (BiGRU) network to capture dynamic features in time series data and a dual-view feature encoding model with sentence-aware and entity-aware capabilities to extract clinical text features from global semantic and local concept perspectives, respectively. Furthermore, we introduce a memory-driven cross-modal attention mechanism, which dynamically establishes deep correlations between clinical text and time series features through learnable memory matrices. In addition, we also introduce a memory-aware constrained layer normalization to alleviate the challenges of multi-modal feature heterogeneity. Besides, we use gating mechanisms and dynamic memory components to enable the model to learn feature information of different historical-current patterns, further improving the model’s performance. Lastly, we combine the integrated gradients for feature attribution analysis to enhance the model’s interpretability. Finally, we evaluate the model’s performance on the MIMIC-III dataset, and the experimental results demonstrate that the model outperforms current advanced baselines in clinical outcome prediction tasks. Notably, our model maintains high predictive accuracy and robustness even when faced with imbalanced data. It can also provide a new perspective for researchers in the field of AI medicine. Full article

Reading comprehension in Chinese as a second language (L2 Chinese) presents unique challenges due to the language’s logographic writing system. Analysis of oral reading miscues reveals specific patterns in L2 learners’ reading processes and comprehension difficulties. Despite established theoretical frameworks for miscue analysis in alphabetic languages, empirical research on miscues in logographic systems such as Chinese remains limited, particularly regarding their relationship with reading comprehension. This study investigates the relationship between oral reading miscues and literal comprehension of Chinese texts among L2 Chinese learners. Sixty-six intermediate-level Chinese learners from U.S. universities participated in the study. Oral reading and sentence-level translation tasks were administered to examine miscues and assess comprehension. Through analyzing the oral reading data, we identified 14 types of oral reading miscues, and they were categorized into four categories: orthographic, syntactic, semantic, and word processing miscues. Results showed strong negative correlations between oral reading miscues and comprehension. Orthographic, syntactic, and semantic miscues were negatively correlated with reading comprehension performance, while word processing miscues showed no significant correlation with comprehension. The findings reveal the complex relationship between character recognition, word processing behaviors, and comprehension in L2 Chinese reading, and suggest a need for a nuanced approach to oral reading error correction in L2 Chinese reading instruction. Based on the findings, pedagogical implications for effective reading instruction and reading assessment in L2 Chinese classrooms are discussed. Full article

Background/Objectives: Previous research has shown that listeners may use acoustic cues for speech processing that are perceived during brief segments in the noise when there is an optimal signal-to-noise ratio (SNR). This “glimpsing” effect requires higher cognitive skills than the speech tasks used in typical audiometric evaluations. Purpose: The aim of this study was to investigate the use of an online test of speech processing in noise in listeners with typical hearing sensitivity (TH, defined as thresholds ≤ 25 dB HL) who were asked to determine the gender of the subject in sentences that were presented in increasing levels of continuous and interrupted noise. Methods: This was a repeated-measures design with three factors (SNR, noise type, and syntactic complexity). Study Sample: Participants with self-reported TH (N = 153, ages 18–39 years, mean age = 20.7 years) who passed an online hearing screening were invited to complete an online questionnaire. Data Collection and Analysis: Participants completed a sentence recognition task under four SNRs (−6, −9, −12, and −15 dB), two syntactic complexity settings (subjective-relative and objective-relative center-embedded), and two noise types (interrupted and continuous). They were asked to listen to 64 sentences through their own headphones/earphones that were presented in an online format at a user-selected comfortable listening level. Their task was to identify the gender of the person performing the action in each sentence. Results: Significant main effects of all three factors as well as the SNR by noise-type two-way interaction were identified (p < 0.05). This interaction indicated that the effect of SNR on sentence comprehension was more pronounced in the continuous noise compared to the interrupted noise condition. Conclusions: Listeners with self-reported TH benefited from the glimpsing effect in the interrupted noise even under low SNRs (i.e., −15 dB). The evaluation of glimpsing may be a sensitive measure of auditory processing beyond the traditional word recognition used in clinical evaluations in persons who report hearing challenges and may hold promise for the development of auditory training programs. Full article

With the widespread application of Android devices in the energy sector, an increasing number of applications rely on SDKs to access privacy-sensitive data, such as device identifiers, location information, energy consumption, and user behavior. However, these data are often stored in different formats and naming conventions, which poses challenges for consistent extraction and identification. Traditional taint analysis methods are inefficient in identifying these entities, hindering the realization of accurate identification. To address this issue, we first propose a high-quality data construction method based on privacy protocols, which includes sentence segmentation, compression encoding, and entity annotation. We then introduce CPS-LSTM (Character-level Privacy-sensitive Entity Adaptive Recognition Model), which enhances the recognition capability of privacy-sensitive entities in mixed Chinese and English text through character-level embedding and word vector fusion. The model features a streamlined architecture, accelerating convergence and enabling parallel sentence processing. Our experimental results demonstrate that CPS-LSTM significantly outperforms the baseline methods in terms of accuracy and recall. The accuracy of CPS-LSTM is 0.09 higher than Lattice LSTM, 0.14 higher than WC-LSTM, and 0.05 higher than FLAT. In terms of recall, CPS-LSTM is 0.07 higher than Lattice LSTM, 0.12 higher than WC-LSTM, and 0.02 higher than FLAT. Full article

This study investigates the cross-linguistic priming effect in the syntactic written output of late bilingual Levantine Arabic speakers who learn English as a second language. In particular, we examined priming sentence type (simple vs. complex sentences) and priming language condition (Levantine Arabic vs. English). Forty-nine bilinguals (Mean age = 33.3, SD = 8.5), who learned Levantine Arabic as their L1 and English as their L2, were primed with a short paragraph presented on the computer screen in either English or Levantine Arabic and asked to produce a written response in the counterpart language. Logistic regression analysis revealed a significant cross-linguistic priming effect, suggesting that the syntactic structure of the prime in the participants’ first language (Levantine Arabic) predicts the participants’ written output in the second language (English), and the reverse is also true. However, there was no significant effect of priming sentence type (simple vs. complex) on the likelihood of producing primed res ponses, indicating that both priming conditions yielded similar levels of priming. In contrast, there was a significant effect of the priming language condition, with participants significantly more likely to produce syntactically primed responses when the priming language was Arabic compared to English. In addition, there was a significant interaction between the priming language condition and priming sentence type: Arabic priming led to more simple sentence production in English, whereas English priming did not significantly affect sentence complexity in Arabic. These findings align with the shared syntax account but highlight the need to consider factors such as language dominance in bilingual syntactic processing. Full article

Situation types can be viewed as the foundation of representation of sentence meaning. Noting that situation types cannot be determined by verbs alone, recent studies often focus on situation type prediction in terms of the combination of different linguistic constituents at the sentence level instead of lexically marked situation types. However, in languages with a fully marked aspectual system, such as Mandarin Chinese, such an approach may miss the opportunity of leveraging lexical aspects as well as other distribution-based lexical cues of event types. Currently, there is a lack of resources and methods for the identification and validation of the lexical aspect, and this issue is particularly severe for Chinese. From a computational linguistics perspective, the main reason for this shortage stems from the absence of a verified lexical aspect classification system, and consequently, a gold-standard dataset annotated according to this classification system. Additionally, owing to the lack of such a high-quality dataset, it remains unclear whether semantic models, including large general-purpose language models, can actually capture this important yet complex semantic information. As a result, the true realization of lexical aspect analysis cannot be achieved. To address these two problems, this paper sets out two objectives. First, we aim to construct a high-quality lexical aspect dataset. Since the classification of the lexical aspect depends on how it interacts with aspectual markers, we establish a scientific classification and data construction process through the selection of vocabulary items, the compilation of co-occurrence frequency matrices, and hierarchical clustering. Second, based on the constructed dataset, we separately evaluate the ability of linguistic features and large language model word embeddings to identify lexical aspect categories in order to (1) verify the capacity of semantic models to infer complex semantics and (2) achieve high-accuracy prediction of lexical aspects. Our final classification accuracy is 72.05%, representing the best result reported thus far. Full article

This study explores the determinants of job satisfaction among IT industry employees in the U.S. and South Korea, focusing on how cultural and socio-economic contexts influence employee well-being and organizational sustainability. Given the high turnover rates in the IT industry, understanding the key factors affecting job satisfaction and dissatisfaction is critical for promoting sustainable organizational practices. By comparing reviews from Glassdoor and Jobplanet, this study uncovers cultural and organizational differences that directly affect employee retention and job satisfaction, offering actionable insights for multinational IT companies seeking to align their strategies with sustainable HR practices. The research utilizes Contextualized Topic Modeling (CTM), a cutting-edge method leveraging Sentence-BERT embeddings, to analyze user-generated reviews. CTM identifies key topics such as attendance management, career development, organizational culture, welfare support, salary, and job autonomy, revealing distinct cultural influences. For example, attendance management and organizational culture positively influenced job satisfaction in U.S. companies, while welfare benefits were more significant in the U.S. than in South Korea. Salary had a positive effect in South Korea but a negative effect in the U.S. The comparative analysis also highlights the higher satisfaction levels among current employees, underscoring factors critical for long-term retention. This study contributes to the literature by leveraging user-generated content to reveal context-specific factors, including new elements such as “technological capabilities” and “location and accessibility,” which are often overlooked in traditional survey-based research. The findings provide actionable insights for IT companies to refine HR practices in ways that enhance employee satisfaction, contributing to both organizational sustainability and the well-being of employees. Full article

In this study, we present the results of experiments on text simplification for the Lithuanian language, where we aim to simplify administrative-style texts to the Plain Language level. We selected mT5, mBART, and LT-Llama-2 as the foundational models and fine-tuned them for the text simplification task. Additionally, we evaluated ChatGPT for this purpose. Also, we conducted a comprehensive assessment of the simplification results provided by these models both quantitatively and qualitatively. The results demonstrated that mBART was the most effective model for simplifying Lithuanian administrative text, achieving the highest scores across all the evaluation metrics. A qualitative evaluation of the simplified sentences complemented our quantitative findings. Attention analysis provided insights into model decisions, highlighting strengths in lexical and syntactic simplifications but revealing challenges with longer, complex sentences. Our findings contribute to advancing text simplification for lesser-resourced languages, with practical applications for more effective communication between institutions and the general public, which is the goal of Plain Language. Full article

Aspect category sentiment analysis aims to automatically identify the sentiment polarities of aspect categories mentioned in text, and is widely used in the data analysis of product reviews and social media. Most existing studies typically limit themselves to utilizing sentence-level local information, thereby failing to fully exploit the potential of document-level and corpus-level global information. To address these limitations, we propose a model that integrates global information for aspect category sentiment analysis, aiming to leverage sentence-level, document-level, and corpus-level information simultaneously. Specifically, based on sentences and their corresponding aspect categories, a graph neural network is initially built to capture document-level information, including sentiment consistency within the same category and sentiment similarity between different categories in a review. We subsequently employ a memory network to retain corpus-level information, where the representations of training instances serve as keys and their associated labels as values. Additionally, a k-nearest neighbor retrieval mechanism is used to find training instances relevant to a given input. Experimental results on four commonly used datasets from SemEval 2015 and 2016 demonstrate the effectiveness of our model. The in-depth experimental analysis reveals that incorporating document-level information substantially improves the accuracies of the two primary ‘positive’ and ‘negative’ categories, while the inclusion of corpus-level information is especially advantageous for identifying the less frequently occurring ‘neutral’ category. Full article

Aspect-level sentiment analysis (ALSA) aims to identify the sentiment polarity associated with specific aspects in textual data. However, existing methods utilizing graph convolutional networks (GCNs) face significant challenges, particularly in analyzing sentiments for multi-word aspects and capturing sentiment relationships across multiple aspects in complex sentences. To address these issues, we introduce the Specific-aspect and Inter-aspect Graph Convolutional Network (SI-GCN), which integrates contextual information, syntactic dependencies, and commonsense knowledge to provide a robust solution. The SI-GCN model incorporates several innovative components: a Specific-aspect GCN module that effectively captures sentiment features for individual aspects; a knowledge-enhanced heterogeneous graph designed to manage implicit sentiment expressions and multi-word aspects; and a dual affine attention mechanism that accurately models inter-aspect relationships. Compared to existing state-of-the-art methods, the SI-GCN achieves improvements in performance ranging from 0.9% to 2.3% across four benchmark datasets. A detailed analysis of text semantics shows that the SI-GCN excels in challenging scenarios, including those involving aspects without explicit sentiment indicators, multi-word aspects, and informal language structures. Full article