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16 pages, 2833 KiB  
Article
MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
by Jianqiang Zhang, Renyao Chen, Shengwen Li, Tailong Li and Hong Yao
Algorithms 2024, 17(12), 593; https://doi.org/10.3390/a17120593 - 23 Dec 2024
Abstract
Geographic knowledge graph representation learning embeds entities and relationships in geographic knowledge graphs into a low-dimensional continuous vector space, which serves as a basic method that bridges geographic knowledge graphs and geographic applications. Previous geographic knowledge graph representation methods primarily learn the vectors [...] Read more.
Geographic knowledge graph representation learning embeds entities and relationships in geographic knowledge graphs into a low-dimensional continuous vector space, which serves as a basic method that bridges geographic knowledge graphs and geographic applications. Previous geographic knowledge graph representation methods primarily learn the vectors of entities and their relationships from their spatial attributes and relationships, which ignores various semantics of entities, resulting in poor embeddings on geographic knowledge graphs. This study proposes a two-stage multimodal geographic knowledge graph representation (MGKGR) model that integrates multiple kinds of semantics to improve the embedding learning of geographic knowledge graph representation. Specifically, in the first stage, a spatial feature fusion method for modality enhancement is proposed to combine the structural features of geographic knowledge graphs with two modal semantic features. In the second stage, a multi-level modality feature fusion method is proposed to integrate heterogeneous features from different modalities. By fusing the semantics of text and images, the performance of geographic knowledge graph representation is improved, providing accurate representations for downstream geographic intelligence tasks. Extensive experiments on two datasets show that the proposed MGKGR model outperforms the baselines. Moreover, the results demonstrate that integrating textual and image data into geographic knowledge graphs can effectively enhance the model’s performance. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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<p>Multimodal data in the geographic knowledge graph provides semantic information for geographic attribute prediction.</p>
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<p>The framework of the proposed MGKGR. (<b>A</b>) Multimodal GeoKG Encoding module processes the multimodal data of multimodal GeoKG for effective encoding. (<b>B</b>) Two-Stage Multimodal Feature Fusion module integrates features from multiple modalities to generate the multimodal features of multimodal GeoKG.</p>
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<p>Model performance on attribute relations, adjacency relations, and mixed relations.</p>
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17 pages, 471 KiB  
Article
Incorporating Global Information for Aspect Category Sentiment Analysis
by Heng Wang, Chen Wang, Chunsheng Li and Changxing Wu
Electronics 2024, 13(24), 5020; https://doi.org/10.3390/electronics13245020 - 20 Dec 2024
Viewed by 275
Abstract
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 [...] Read more.
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
(This article belongs to the Section Artificial Intelligence)
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<p>Examples of document-level global information.</p>
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<p>The aspect category sentiment analysis model with global information.</p>
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<p><math display="inline"><semantics> <msub> <mi>F</mi> <mn>1</mn> </msub> </semantics></math> scores for each sentiment polarity after incorporating the global contexts.</p>
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22 pages, 2584 KiB  
Article
Leveraging AI and Data Visualization for Enhanced Policy-Making: Aligning Research Initiatives with Sustainable Development Goals
by Maicon Herverton Lino Ferreira da Silva Barros, Leonides Medeiros Neto, Guto Leoni Santos, Roberto Cesar da Silva Leal, Raysa Carla Leal da Silva, Theo Lynn, Raphael Augusto Dourado and Patricia Takako Endo
Sustainability 2024, 16(24), 11050; https://doi.org/10.3390/su162411050 - 17 Dec 2024
Viewed by 430
Abstract
Scientists, research institutions, funding agencies, and policy-makers have all emphasized the need to monitor and prioritize research investments and outputs to support the achievement of the United Nations Sustainable Development Goals (SDGs). Unfortunately, many current and historic research publications, proposals, and grants were [...] Read more.
Scientists, research institutions, funding agencies, and policy-makers have all emphasized the need to monitor and prioritize research investments and outputs to support the achievement of the United Nations Sustainable Development Goals (SDGs). Unfortunately, many current and historic research publications, proposals, and grants were not categorized against the SDGs at the time of submission. Manual post hoc classification is time-consuming and prone to human biases. Even when classified, few tools are available to decision makers for supporting resource allocation. This paper aims to develop a deep learning classifier for categorizing research abstracts by the SDGs and a decision support system for research funding policy-makers. First, we fine-tune a Bidirectional Encoder Representations from Transformers (BERT) model using a dataset of 15,488 research abstracts from authors at leading Brazilian universities, which were preprocessed and balanced for training and testing. Second, we present a PowerBI dashboard that visualizes classifications for supporting informed resource allocation for sustainability-focused research. The model achieved an F1-score, precision, and recall exceeding 70% for certain classes and successfully classified existing projects, thereby enabling better tracking of Agenda 2030 progress. Although the model is capable of classifying any text, it is specifically optimized for Brazilian research due to the nature of its fine-tuning data. Full article
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<p>Methodology used in this work.</p>
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<p>Prompt engineering.</p>
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<p>The developed dashboard interface, divided in tabs for overview (<b>left</b>) and ODS-based analysis (<b>right</b>). The main components are (A) menu for funding program selection (B) tab selector (C) overview tab filters panel (D/G) KPIs panel (E) proportion of projects related/unrelated with SDGs (F) SDG tab filters panel.</p>
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23 pages, 1149 KiB  
Article
MGAFN-ISA: Multi-Granularity Attention Fusion Network for Implicit Sentiment Analysis
by Yifan Huo, Ming Liu, Junhong Zheng and Lili He
Electronics 2024, 13(24), 4905; https://doi.org/10.3390/electronics13244905 (registering DOI) - 12 Dec 2024
Viewed by 378
Abstract
Although significant progress has been made in sentiment analysis tasks based on image–text data, existing methods still have limitations in capturing cross-modal correlations and detailed information. To address these issues, we propose a Multi-Granularity Attention Fusion Network for Implicit Sentiment Analysis (MGAFN-ISA). MGAFN-ISA [...] Read more.
Although significant progress has been made in sentiment analysis tasks based on image–text data, existing methods still have limitations in capturing cross-modal correlations and detailed information. To address these issues, we propose a Multi-Granularity Attention Fusion Network for Implicit Sentiment Analysis (MGAFN-ISA). MGAFN-ISA that leverages neural networks and attention mechanisms to effectively reduce noise interference between different modalities and captures distinct, fine-grained visual and textual features. The model includes two key feature extraction modules: a multi-scale attention fusion-based visual feature extractor and a hierarchical attention mechanism-based textual feature extractor, each designed to extract detailed and discriminative visual and textual representations. Additionally, we introduce an image translator engine to produce accurate and detailed image descriptions, further narrowing the semantic gap between the visual and textual modalities. A bidirectional cross-attention mechanism is also incorporated to utilize correlations between fine-grained local regions across modalities, extracting complementary information from heterogeneous visual and textual data. Finally, we designed an adaptive multimodal classification module that dynamically adjusts the contribution of each modality through an adaptive gating mechanism. Extensive experimental results demonstrate that MGAFN-ISA achieves a significant performance improvement over nine state-of-the-art methods across multiple public datasets, validating the effectiveness and advancement of our proposed approach. Full article
(This article belongs to the Section Artificial Intelligence)
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<p>Examples of multimodal sentiment analysis.</p>
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<p>The overall structure of the proposed MGAFN-ISA method. The visual–text feature extraction utilizes ResNet-101 to extract multi-scale image features, combined with ViT-GPT-2 to generate image descriptions, while BERT is used to extract textual features. The multimodal fine-grained correlation fusion module adopts a bidirectional cross-attention mechanism to capture fine-grained associations between visual and textual modalities. Furthermore, multi-head attention and layer stacking are used to enhance feature interactions. The classification module employs an adaptive gating mechanism to dynamically fuse visual and textual features, achieving accurate classification of multimodal implicit sentiment by integrating the improved focal loss function.</p>
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<p>The performance of MGAFN-ISA under different proportions of training data on each dataset.</p>
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19 pages, 528 KiB  
Article
Enhancing Word Embeddings for Improved Semantic Alignment
by Julian Szymański, Maksymilian Operlejn and Paweł Weichbroth
Appl. Sci. 2024, 14(24), 11519; https://doi.org/10.3390/app142411519 - 10 Dec 2024
Viewed by 459
Abstract
This study introduces a method for the improvement of word vectors, addressing the limitations of traditional approaches like Word2Vec or GloVe through introducing into embeddings richer semantic properties. Our approach leverages supervised learning methods, with shifts in vectors in the representation space enhancing [...] Read more.
This study introduces a method for the improvement of word vectors, addressing the limitations of traditional approaches like Word2Vec or GloVe through introducing into embeddings richer semantic properties. Our approach leverages supervised learning methods, with shifts in vectors in the representation space enhancing the quality of word embeddings. This ensures better alignment with semantic reference resources, such as WordNet. The effectiveness of the method has been demonstrated through the application of modified embeddings to text classification and clustering. We also show how our method influences document class distributions, visualized through PCA projections. By comparing our results with state-of-the-art approaches and achieving better accuracy, we confirm the effectiveness of the proposed method. The results underscore the potential of adaptive embeddings to improve both the accuracy and efficiency of semantic analysis across a range of NLP. Full article
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<p>Classification results for the nearest neighbor method.</p>
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<p>Classification results for the random forest classifier with mean sentence embedding.</p>
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<p>PCA visualization of original embeddings.</p>
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<p>PCA visualization of neural embeddings.</p>
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<p>PCA visualization of fine-tuned embeddings.</p>
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<p>PCA visualization of geometrical embeddings.</p>
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<p>Results for usage of mean category distance.</p>
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<p>Results for mean category distance by category.</p>
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<p>Results for Category Density.</p>
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<p>Results for category density by category.</p>
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15 pages, 1457 KiB  
Article
A Chinese Short Text Similarity Method Integrating Sentence-Level and Phrase-Level Semantics
by Zhenji Shen and Zhiyong Xiao
Electronics 2024, 13(24), 4868; https://doi.org/10.3390/electronics13244868 - 10 Dec 2024
Viewed by 383
Abstract
Short text similarity, as a pivotal research domain within Natural Language Processing (NLP), has been extensively utilized in intelligent search, recommendation systems, and question-answering systems. Most existing short-text similarity models focus on aligning the overall semantic content of an entire sentence, often ignoring [...] Read more.
Short text similarity, as a pivotal research domain within Natural Language Processing (NLP), has been extensively utilized in intelligent search, recommendation systems, and question-answering systems. Most existing short-text similarity models focus on aligning the overall semantic content of an entire sentence, often ignoring the semantic associations between individual phrases in the sentence. It is particular in the Chinese context, as synonyms and near-synonyms can cause serious interference in the computation of text similarity. To overcome these limitations, a novel short text similarity computation method integrating both sentence-level and phrase-level semantics was proposed. By harnessing vector representations of Chinese words/phrases as external knowledge, this approach amalgamates global sentence characteristics with local phrase features to compute short text similarity from diverse perspectives, spanning from the global to the local level. Experimental results demonstrate that the proposed model outperforms previous methods in the Chinese short text similarity task. Specifically, the model achieves an accuracy of 90.16% in LCQMC, which is 2.23% and 1.46%, respectively, better than ERNIE and Glyce + BERT. Full article
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<p>Overall architecture diagram.</p>
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<p>External knowledge processing.</p>
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<p>Feature fusion and similarity computation diagram.</p>
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14 pages, 605 KiB  
Article
Order, Identity, and the “New Self”: Reading Ephesians Through Social Representations Theory
by William B. Bowes
Religions 2024, 15(12), 1506; https://doi.org/10.3390/rel15121506 - 10 Dec 2024
Viewed by 464
Abstract
Social representations theory (SRT) refers to an approach within social psychology focusing on systems of beliefs, concepts, and values that establish social order and allow for individuals and groups to identify and understand themselves vis-à-vis others. It involves the ascription of meaning to [...] Read more.
Social representations theory (SRT) refers to an approach within social psychology focusing on systems of beliefs, concepts, and values that establish social order and allow for individuals and groups to identify and understand themselves vis-à-vis others. It involves the ascription of meaning to phenomena so that the unfamiliar is made familiar, and new concepts are integrated into existing worldviews in an ongoing process of constructing and interpreting social realities. This approach has not yet been applied to any biblical texts, and this article will explore how such an application would prove fruitful for understanding the processes of identification and community formation in early Christian groups, with a specific focus on Ephesians. This study will focus on how the concepts of reconciled differences (Eph 2.11–22) and of the “new self” (Eph 4.17–32) are communicated to the readers. Analyzing these concepts through SRT will elucidate how the author advocates for certain beliefs, concepts, and values as part of the community members’ process of aligning themselves with their newly created self. Reading Ephesians through SRT can better elucidate how the text reflects its enigmatic community, which was being formed and reformed through identification, division, and re-identification in the tumultuous second half of the first century. Full article
(This article belongs to the Special Issue Resurrection and New Creation in Ephesians)
32 pages, 777 KiB  
Article
A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
by Jothi Prakash Venugopal, Arul Antran Vijay Subramanian, Gopikrishnan Sundaram, Marco Rivera and Patrick Wheeler
Appl. Sci. 2024, 14(23), 11471; https://doi.org/10.3390/app142311471 - 9 Dec 2024
Viewed by 672
Abstract
Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced [...] Read more.
Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications. Full article
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<p>Architecture of the proposed method.</p>
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<p>Confusion matrices for the proposed method and other models.</p>
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<p>ROC curves for the proposed method, baseline models, and existing models in the literature.</p>
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<p>Trend analysis of model performance over six months.</p>
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18 pages, 2188 KiB  
Article
Event Argument Extraction for Rainstorm Disasters Based on Social Media: A Case Study of the 2021 Heavy Rains in Henan
by Yun He, Banghui Yang, Haixia He, Xianyun Fei, Xiangtao Fan and Jian Liu
Water 2024, 16(23), 3535; https://doi.org/10.3390/w16233535 - 8 Dec 2024
Viewed by 498
Abstract
Rainstorm disasters have wide-ranging impacts on communities, but traditional information collection methods are often hampered by high labor costs and limited coverage. Social media platforms such as Weibo provide new opportunities for monitoring and analyzing disaster-related information in real-time. In this paper, we [...] Read more.
Rainstorm disasters have wide-ranging impacts on communities, but traditional information collection methods are often hampered by high labor costs and limited coverage. Social media platforms such as Weibo provide new opportunities for monitoring and analyzing disaster-related information in real-time. In this paper, we present ETEN_BERT_QA, a novel model for extracting event arguments from Weibo rainstorm disaster texts. The model incorporates the event text enhancement network (ETEN) to enhance the extraction process by improving the semantic representation of event information in combination with event trigger words. To support our approach, we constructed RainEE, a dataset dedicated to rainstorm disaster event extraction, and implemented a two-step process, as follows: (1) event detection, which identifies trigger words and classifies them into event types, and (2) event argument extraction, which identifies event arguments and classifies them into argument roles. Our ETEN_BERT_QA model combines ETEN with a BERT-based question-answering mechanism to further improve the understanding of the event text. Experimental evaluations on the RainEE and DuEE datasets show that ETEN_BERT_QA significantly outperforms the baseline model in terms of accuracy and the number of event argument extractions, validating its effectiveness in analyzing rainstorm disaster-related Weibo texts. Full article
(This article belongs to the Section Urban Water Management)
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<p>Number of comments changing over time.</p>
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<p>Event extraction example.</p>
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<p>Overall flowchart of the RainEE dataset production methodology.</p>
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<p>The structure of BERT_QA, as well as the baseline of our work.</p>
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<p>Event argument extraction structure diagram based on trigger word perception coding event text enhancement.</p>
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14 pages, 1867 KiB  
Article
Pseudo Multi-Modal Approach to LiDAR Semantic Segmentation
by Kyungmin Kim
Sensors 2024, 24(23), 7840; https://doi.org/10.3390/s24237840 - 8 Dec 2024
Viewed by 451
Abstract
To improve the accuracy and reliability of LiDAR semantic segmentation, previous studies have introduced multi-modal approaches that utilize additional modalities, such as 2D RGB images, to provide complementary information. However, these methods increase the cost of data collection, sensor hardware requirements, power consumption, [...] Read more.
To improve the accuracy and reliability of LiDAR semantic segmentation, previous studies have introduced multi-modal approaches that utilize additional modalities, such as 2D RGB images, to provide complementary information. However, these methods increase the cost of data collection, sensor hardware requirements, power consumption, and computational complexity. We observed that multi-modal approaches improve the semantic alignment of 3D representations. Motivated by this observation, we propose a pseudo multi-modal approach. To this end, we introduce a novel class-label-driven artificial 2D image construction method. By leveraging the close semantic alignment between image and text features of vision–language models, artificial 2D images are synthesized by arranging LiDAR class label text features. During training, the semantic information encoded in the artificial 2D images enriches the 3D features through knowledge distillation. The proposed method significantly reduces the burden of training data collection and facilitates more effective learning of semantic relationships in the 3D backbone network. Extensive experiments on two benchmark datasets demonstrate that the proposed method improves performance by 2.2–3.5 mIoU over the baseline using only LiDAR data, achieving performance comparable to that of real multi-modal approaches. Full article
(This article belongs to the Section Sensing and Imaging)
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<p>Visualization of 3D feature distribution across different approaches. (<b>a</b>) Uni-modal, (<b>b</b>) real multi-modal, and (<b>c</b>) proposed pseudo multi-modal LiDAR semantic segmentation frameworks. The real multi-modal method and proposed pseudo multi-modal method exhibit better semantic alignment compared to the uni-modal method, as evidenced by closer distances between class features within the same super-category. The colors for each class in the figure follow the official colormap of SemanticKITTI.</p>
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<p>Overview of proposed pseudo multi-modal LiDAR semantic segmentation framework. Using a vision–language model (e.g., CLIP), we generate pseudo RGB images aligned with LiDAR data. These LiDAR and pseudo RGB pairs are used to train a multi-modal segmentation model.</p>
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<p>Artificial 2D image construction process. (<b>a</b>) Using the CLIP text encoder, we obtain text embeddings corresponding to each class in the dataset. (<b>b</b>) We project the 3D point cloud onto a 2D image plane, mapping each point to a corresponding <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </semantics></math> pixel. (<b>c</b>) For each <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </semantics></math> pixel where a 3D point is projected, we assign the text embedding associated with the point’s class label. By filling the entire 2D image plane in this manner, we construct an artificial 2D image. This process requires only the pre-obtained text embeddings for the class set and the 3D LiDAR point cloud. The colors for each class in the figure follow the official colormap of SemanticKITTI.</p>
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<p>Overall training pipeline of proposed pseudo multi-modal LiDAR semantic segmentation framework. During training, we constructed an artificial 2D image from the input LiDAR data and label, forming (real LiDAR, artificial image) pairs to train the multi-modal segmentation network. During inference, only the input LiDAR passes through the 3D branch to obtain predictions for performance evaluation.</p>
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<p>The distribution of the text embeddings for the SemanticKITTI class set. These results visualize the text embeddings obtained using each of the following text prompt templates. (<b>a</b>) <span class="html-italic">Base</span>: Provides only the default class name, (<b>b</b>) <span class="html-italic">Base + Sup</span>: Includes super-class information, (<b>c</b>) <span class="html-italic">Base + Neg</span>: Explicitly clarify that it is not a similar class. These tSNE plots demonstrate that providing additional information brings semantically similar classes closer together.</p>
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<p>Qualitative examples of segmentation results on the SemanticKITTI validation set. From top to bottom, the figure visualizes the RGB image, ground truth, and results for baseline, 2DPASS, and ours. Each point in the ground truth is colored using the official SemanticKITTI colormap. In the bottom three rows, green points indicate correct predictions, while magenta points represent incorrect predictions. The red dashed circles highlight the differences between predictions. The proposed method achieves more accurate segmentation for thin objects such as tree trunks and fences.</p>
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<p>Additional qualitative examples of segmentation results on the SemanticKITTI validation set. From top to bottom, the figure visualizes the RGB image, ground truth, results for baseline, 2DPASS, and ours. Each point in the ground truth is colored using the official SemanticKITTI colormap. In the bottom three rows, green points indicate correct predictions, while magenta points represent incorrect predictions. The red dashed circles highlight the differences between predictions. The proposed method achieves more accurate predictions in challenging cases where confusion may occur within the same super-category.</p>
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22 pages, 1599 KiB  
Article
Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy
by Chenyang Dong, Shiyu Xi, Yinchao Che, Shufeng Xiong, Xinming Ma, Lei Xi and Shuping Xiong
Algorithms 2024, 17(12), 559; https://doi.org/10.3390/a17120559 - 6 Dec 2024
Viewed by 319
Abstract
Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high [...] Read more.
Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high information density, complex logical structures, large spans between entities, and heterogeneous entity lengths, as well as to overcome the challenges faced when using traditional joint extraction methods, including triplet overlap, exposure bias, and redundant computation, we propose a single-stage entity–relation joint extraction model based on HT-BES multi-dimensional labeling (MD-SERel). First, in the encoding layer, to address the complex structural characteristics of pesticide registration texts, we employ RoBERTa combined with a multi-head self-attention mechanism to capture the deep semantic features of the text. Simultaneously, syntactic features are extracted using a syntactic dependency tree and graph neural networks to enhance the model’s understanding of text structure. Subsequently, we integrate semantic and syntactic features, enriching the character vector representations and thus improving the model’s ability to represent complex textual data. Secondly, in the multi-dimensional labeling framework layer, we use HT-BES multi-dimensional labeling, where the model assigns multiple labels to each character. These labels include entity boundaries, positions, and head–tail entity association information, which naturally resolves overlapping triplets. Through utilizing a parallel scoring function and fine-grained classification components, the joint extraction of entities and relations is transformed into a multi-label sequence labeling task based on relation dimensions. This process does not involve interdependent steps, thus enabling single-stage parallel labeling, preventing exposure bias and reducing computational redundancy. Finally, in the decoding layer, entity–relation triplets are decoded based on the predicted labels from the fine-grained classification. The experimental results demonstrate that the MD-SERel model performs well on both the Pesticide Registration Dataset (PRD) and the general DuIE dataset. On the PRD, compared to the optimal baseline model, the training time is 1.2 times faster, the inference time is 1.2 times faster, and the F1 score is improved by 1.5%, demonstrating its knowledge extraction capabilities in pesticide registration documents. On the DuIE dataset, the MD-SERel model also achieved better results compared to the baseline, demonstrating its strong generalization ability. These findings will provide technical support for the construction of pesticide knowledge bases. Full article
(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))
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<p>MD-SERel model.</p>
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<p>Self-attention mechanism architecture diagram.</p>
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<p>Syntactic dependency matrix. In the <a href="#algorithms-17-00559-f003" class="html-fig">Figure 3</a>, (<b>a</b>) is the result of semantic analysis of example sentences. (<b>b</b>) is a semantic adjacency matrix constructed from (<b>a</b>).</p>
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<p>HT-BES interactive annotation strategy.</p>
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<p>The type and quantity distribution of entities and relations.</p>
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<p>Entity lengths.</p>
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<p>The results for different overlapping patterns of triples.</p>
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<p>The results of different self-attention head numbers.</p>
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21 pages, 968 KiB  
Article
Advancing Author Gender Identification in Modern Standard Arabic with Innovative Deep Learning and Textual Feature Techniques
by Hanen Himdi and Khaled Shaalan
Information 2024, 15(12), 779; https://doi.org/10.3390/info15120779 - 5 Dec 2024
Viewed by 477
Abstract
Author Gender Identification (AGI) is an extensively studied subject owing to its significance in several domains, such as security and marketing. Recognizing an author’s gender may assist marketers in segmenting consumers more effectively and crafting tailored content that aligns with a gender’s preferences. [...] Read more.
Author Gender Identification (AGI) is an extensively studied subject owing to its significance in several domains, such as security and marketing. Recognizing an author’s gender may assist marketers in segmenting consumers more effectively and crafting tailored content that aligns with a gender’s preferences. Also, in cybersecurity, identifying an author’s gender might aid in detecting phishing attempts where hackers could imitate individuals of a specific gender. Although studies in Arabic have mostly concentrated on written dialects, such as tweets, there is a paucity of studies addressing Modern Standard Arabic (MSA) in journalistic genres. To address the AGI issue, this work combines the beneficial properties of natural language processing with cutting-edge deep learning methods. Firstly, we propose a large 8k MSA article dataset composed of various columns sourced from news platforms, labeled with each author’s gender. Moreover, we extract and analyze textual features that may be beneficial in identifying gender-related cues through their writings, focusing on semantics and syntax linguistics. Furthermore, we probe several innovative deep learning models, namely, Convolutional Neural Networks (CNNs), LSTM, Bidirectional LSTM (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). Beyond that, a novel enhanced BERT model is proposed by incorporating gender-specific textual features. Through various experiments, the results underscore the potential of both BERT and the textual features, resulting in a 91% accuracy for the enhanced BERT model and a range of accuracy from 80% to 90% accuracy for deep learning models. We also employ these features for AGI in informal, dialectal text, with the enhanced BERT model reaching 68.7% accuracy. This demonstrates that these gender-specific textual features are conducive to AGI across MSA and dialectal texts. Full article
(This article belongs to the Section Artificial Intelligence)
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<p>Proposed framework.</p>
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<p>Female author word cloud.</p>
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<p>Male author word cloud.</p>
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<p>Word count distribution in columns across genders.</p>
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<p>Character count distribution in columns across genders.</p>
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<p>POS-tagged article using Arabic Pipeline Tool (APL).</p>
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<p>Model architecture for the enhanced BERT.</p>
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<p>Model performance comparison.</p>
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<p>Top 10 important features.</p>
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33 pages, 1325 KiB  
Article
A Centrality-Weighted Bidirectional Encoder Representation from Transformers Model for Enhanced Sequence Labeling in Key Phrase Extraction from Scientific Texts
by Tsitsi Zengeya, Jean Vincent Fonou Dombeu and Mandlenkosi Gwetu
Big Data Cogn. Comput. 2024, 8(12), 182; https://doi.org/10.3390/bdcc8120182 - 4 Dec 2024
Viewed by 523
Abstract
Deep learning approaches, utilizing Bidirectional Encoder Representation from Transformers (BERT) and advanced fine-tuning techniques, have achieved state-of-the-art accuracies in the domain of term extraction from texts. However, BERT presents some limitations in that it primarily captures the semantic context relative to the surrounding [...] Read more.
Deep learning approaches, utilizing Bidirectional Encoder Representation from Transformers (BERT) and advanced fine-tuning techniques, have achieved state-of-the-art accuracies in the domain of term extraction from texts. However, BERT presents some limitations in that it primarily captures the semantic context relative to the surrounding text without considering how relevant or central a token is to the overall document content. There has also been research on the application of sequence labeling on contextualized embeddings; however, the existing methods often rely solely on local context for extracting key phrases from texts. To address these limitations, this study proposes a centrality-weighted BERT model for key phrase extraction from text using sequence labelling (CenBERT-SEQ). The proposed CenBERT-SEQ model utilizes BERT to represent terms with various contextual embedding architectures, and introduces a centrality-weighting layer that integrates document-level context into BERT. This layer leverages document embeddings to influence the importance of each term based on its relevance to the entire document. Finally, a linear classifier layer is employed to model the dependencies between the outputs, thereby enhancing the accuracy of the CenBERT-SEQ model. The proposed CenBERT-SEQ model was evaluated against the standard BERT base-uncased model using three Computer Science article datasets, namely, SemEval-2010, WWW, and KDD. The experimental results show that, although the CenBERT-SEQ and BERT-base models achieved higher and close comparable accuracy, the proposed CenBERT-SEQ model achieved higher precision, recall, and F1-score than the BERT-base model. Furthermore, a comparison of the proposed CenBERT-SEQ model to that of related studies revealed that the proposed CenBERT-SEQ model achieved a higher accuracy, precision, recall, and F1-score of 95%, 97%, 91%, and 94%, respectively, than related studies, showing the superior capabilities of the CenBERT-SEQ model in keyphrase extraction from scientific documents. Full article
(This article belongs to the Special Issue Advances in Natural Language Processing and Text Mining)
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<p>Sequence labeling method for keyword extraction and the extraction of the color annotation part into the text’s keywords and corresponding tags.</p>
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<p>Architecture of the proposed centrality-weighted BERT model.</p>
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<p>A sample abstract from a Semeval2010 dataset.</p>
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<p>Computational time for CenBERT-SEQ and BERT-base models.</p>
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<p>ROC curves showing the performance of the CenBERT-SEQ model on Semeval2010 dataset.</p>
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<p>ROC curves showing the performance of the CenBERT-SEQ model on KDD dataset.</p>
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<p>ROC curves showing the performance of the CenBERT-SEQ model on WWW dataset.</p>
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<p>Charts of the evaluation of CenBERT-SEQ model during training.</p>
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<p>Charts of evaluation of BERT-base model during training.</p>
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<p>Confusion matrices for the BERT-base and CenBERT-SEQ models.</p>
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<p>Key term identification results for CenBERT-SEQ on the SemEval-2010 dataset.</p>
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<p>Key term identification results for the BERT-base model on the SemEval-2010 dataset.</p>
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<p>Performance metrics for CenBERT-SEQ on the PubMed dataset.</p>
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16 pages, 1602 KiB  
Article
Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
by Meryem Chajia and El Habib Nfaoui
Future Internet 2024, 16(12), 453; https://doi.org/10.3390/fi16120453 - 3 Dec 2024
Viewed by 769
Abstract
Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new [...] Read more.
Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embeddings from large language models and logistic regression to accurately identify churners. We conducted extensive experiments using a range of embedding techniques, including OpenAI Text-embedding, Google Gemini Text Embedding, bidirectional encoder representations from transformers (BERT), Sentence-Transformers, Sent2vec, and Doc2vec, to extract meaningful features. Additionally, we tested various classifiers, including logistic regression, support vector machine, random forest, K-nearest neighbors, multilayer perceptron, naive Bayes, decision tree, and zero-shot classification, to build a robust model capable of making accurate predictions. The best-performing model in our experiments is the logistic regression classifier, which we trained using the extracted feature from the OpenAI Text-embedding-ada-002 model, achieving an accuracy of 89%. The proposed model demonstrates a high discriminative ability between churning and loyal customers. Full article
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<p>Zero-Shot Classifier Methodology.</p>
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<p>Multilayer perceptron (MLP) architecture.</p>
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<p>Churn Predictive Model Building Methodology.</p>
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<p>Final Model Deployment.</p>
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28 pages, 5792 KiB  
Article
Enabling Perspective-Aware Ai with Contextual Scene Graph Generation
by Daniel Platnick, Marjan Alirezaie and Hossein Rahnama
Information 2024, 15(12), 766; https://doi.org/10.3390/info15120766 - 2 Dec 2024
Viewed by 659
Abstract
This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on multimodal data—such as text, audio, and images—challenges in data collection, alignment, and [...] Read more.
This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on multimodal data—such as text, audio, and images—challenges in data collection, alignment, and privacy have led us to focus on enabling the contextual understanding of images. To achieve this, we developed perspective-aware scene graph generation with LLM post-processing (PASGG-LM). This framework extends traditional scene graph generation (SGG) by incorporating large language models (LLMs) to enhance contextual understanding. PASGG-LM integrates classical scene graph outputs with LLM post-processing to infer richer contextual information, such as emotions, activities, and social contexts. To test PASGG-LM, we introduce the context-aware scene graph generation task, where the goal is to generate a context-aware situation graph describing the input image. We evaluated PASGG-LM pipelines using state-of-the-art SGG models, including Motifs, Motifs-TDE, and RelTR, and showed that fine-tuning LLMs, particularly GPT-4o-mini and Llama-3.1-8B, improves performance in terms of R@K, mR@K, and mAP. Our method is capable of generating scene graphs that capture complex contextual aspects, advancing human–machine interaction by enhancing the representation of diverse perspectives. Future directions include refining contextual scene graph models and expanding multi-modal data integration for PAi applications in domains such as healthcare, education, and social robotics. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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<p>Chronicle pipeline consisting of two phases: (1) construction of the chronicle as a reason-ready structure, and (2) utilization, where the chronicle is communicated and queried by users to share the individual’s captured perspective. The colouring indicates new sub-graphs being extended in the chronicle throughout the chronological structural learning process.</p>
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<p>Representation of the situation graph derived from the DOLCE Ultralite (DUL) ontology.</p>
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<p>Distribution of the top 30 most frequent objects occurring in scene graphs of the PAi SGG dataset.</p>
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<p>Distribution of the 12 new PAi predicates as they appear in scene graphs of the PAi dataset.</p>
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<p>Comparison of PSS performance for three SGG models (Motifs-TDE, Motifs, RelTR) on the PAi SGG dataset, illustrating their difficulty in capturing context-based aspects for perspective-aware computing. The performance is also shown with PAi feature exclusion to explore potential improvements.</p>
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<p>Comparison of generated scene graphs between Neural Motifs-TDE (<b>left</b>) and our proposed PASGG-LM pipeline (<b>right</b>) evaluated on a PAi image from the unbiased hold-out set. In this example, PASGG-LM uses Motifs-TDE, with the generated scene graphs processed by GPT-4o-mini fine-tuned on situation graphs from our PAi SGG data.</p>
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<p>Predicate classes with recall scores of zero, including activity (participant-specific), has emotion (participant-specific), has participant, and has time, were omitted.</p>
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<p>Comparison of PSS performance for PASGG-LM across three SGG models (Motif-TDE, Motifs, RelTR) with and without fine-tuning using two LLM models: GPT-4o and Llama-3.1-8B. The figure highlights the improvements achieved through fine-tuning, with GPT-4o consistently outperforming Llama-3.1-8B in both settings.</p>
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