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A comparative study of One-Hot,TF-IDF, and Word2Vec for Classifying Illegal Advertising Texts 24

A comparative study of One-Hot,TF-IDF, and Word2Vec for Clas...

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8th International conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Zhang, Yuedan He, Lingmin Zhang, Yunpeng Zhao, Panzhi Zhang, Bole Cheng, Fang Liangxin Honors College College of Information Engineering China Jiliang University Zhejiang Hangzhou China Department of Computer Science College of Information Engineering Big Data Laboratory China Jiliang University Zhejiang Hangzhou China College of Information Engineering China Jiliang University Zhejiang Hangzhou China

ISBN: (纸本)9798400717383

With the vigorous development of the Internet advertising industry, the increasing number of illegal advertisements has brought misleading information and risks to consumers, while also posing challenges to market order. Therefore, how to efficiently and accurately identify and classify illegal advertisements has become an urgent issue that needs to be addressed. This paper delves into this topic, starting from the traditional one-hot encoding approach and progressing to the study of the TF-IDF method. However, when the text data vectorized by these methods is fed into the Support Vector Machine (SVM) model, it becomes evident that both approaches lack semantic information, which is crucial for identifying illegal advertisement texts. To address this limitation, this paper subsequently introduces the study of Word2Vec and employs grid search to find the optimal parameters for the SVM model. This enhanced approach significantly improves the accuracy and efficiency of illegal advertisement recognition, raising the model's accuracy from the initial 69.8 percent to 88.3 percent, thereby contributing to the creation of a healthier and more transparent advertising environment. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing systems

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Using language Models for assessment of users’ satisfaction with their partner in Persian 1

Using Language Models for assessment of users’ satisfaction...

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1st Workshop on language Models for Low-Resource languages, LoResLM 2025 - co-located at the 31st International conference on Computational Linguistics, COLING 2025

作者： Habibzadeh, Zahra Asadpour, Masoud School of Electrical Computer Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9798891762152

Sentiment analysis, the process of gauging user attitudes and emotions through their textual data, including social media posts and other forms of communication, is a valuable tool for informed decision-making. In other words, by determining whether a statement conveys positivity, negativity, or neutrality, sentiment analysis offers insights into public sentiment regarding a product, individual, event, or other significant topics. This research focuses on the effectiveness of sentiment analysis techniques, using Machine Learning (ML) and natural language processing (NLP) especially pre-trained language models for Persian, in assessing users’ satisfaction with their partner, using data collected from X (formerly Twitter). Our motivation stems from traditional in-person surveys, which periodically analyze societal challenges in Iran. The limitations of these surveys led us to explore Artificial Intelligence (AI) as an alternative solution for addressing contemporary social issues. We collected Persian tweets and utilized data annotation techniques to label them according to our research question, forming the dataset. Our goal also was to provide a benchmark of Persian tweets on this specific topic. To evaluate our dataset, we employed several classification methods, including classical ML models, Deep Neural Networks, and pre-trained language models for Persian. Following a comprehensive evaluation, our results show that BERTweet-FA (one of the pre-trained language models for Persian) emerged as the best performer among the classifiers for assessing users’ satisfaction. This point indicates the ability of language models to understand conversational Persian text and perform sentiment analysis, even in a low-resource language like Persian. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Information Extraction to Identify Novel Technologies and Trends in Renewable Energy 24th

Information Extraction to Identify Novel Technologies and Tr...

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24th International TRIZ Future conference

作者： MacLean, Connor Cavallucci, Denis INSA Strasbourg 24 Bd Victoire F-67000 Strasbourg France

ISBN: (纸本)9783031759253;9783031759239;9783031759222

Achieving carbon neutrality by 2050 requires unprecedented technological, economic, and sociological changes. With time as a scarce resource, it is crucial to base decisions on relevant facts and information to avoid misdirection. This study aims to help decision makers quickly find relevant information related to companies and organizations in the renewable energy sector. Over the course of this PhD program, we will propose several text-mining methods applied to the renewable energy sector in order to detect technological breakthroughs and new, innovative companies. These techniques include specialized Named Entity Recognition (NER) models, news summarization, and trend analysis of scientific articles. Further steps in this project will contain a TRIZ-based analysis of scientific articles in order to attribute a multi-factor score on the innovative potential of novel technologies.

关键词： natural language processing Named Entity Recognition Topic Modeling Renewable Energy TRIZ

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Complex Open Information Extraction with Heterogeneous Syntax Forests

Complex Open Information Extraction with Heterogeneous Synta...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Meishan Guo, Peiming Fei, Hao Zhang, Min China National University of Singapore Singapore

ISBN: (纸本)9798350368741

Open Information Extraction (OIE) aims at extracting the relational triplets from open-domain texts. Existing methods, unfortunately, mostly fall prey to the complex OIE setting, due to the failure to extract unseen words and underutilize syntactic features. In this work, we propose a novel system tailored for complex OIE, where a generative PLM with non-autoregressive generative decoding is adopted for abstractive OIE generation. We introduce a heterogeneous syntactic forests as features for the task, merging constituency and dependency forests into a unified syntax graph, aiding in better detecting potential boundaries and relationships within complex terms. Also an Implicit-Explicit Contrastive Learning mechanism is devised to boost the perception of implicit relations and spans. Our system outperforms the current state-of-the-art model across seven OIE datasets. © 2025 IEEE.

关键词： Generative language model Open information extraction Syntactic feature

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Research on traffic congestion detection methods based on deep learning 24

Research on traffic congestion detection methods based on de...

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3rd International conference on Signal processing, Computer Networks and Communications, SPCNC 2024

作者： Yang, Deguo Li, Nan Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400710834

With the rapid development of China’s economy and the advancement of urbanization, China’s per capita automobile ownership rate is also increasing year by year. But with the increasing number of cars, the problem of traffic congestion in cities is becoming increasingly serious. The prediction and analysis of the road status can provide a basis for the urban road traffic planning and guidance. Therefore, it is necessary to study the traffic prediction and improve the accuracy of traffic state prediction. Deep learning can learn and extract features from massive data;suitable for various fields, such as image recognition, speech recognition, natural language processing, etc., with good scalability and flexibility, can improve performance by increasing the network level and the number of neurons, and can conduct autonomous learning and iterative optimization. With the increase of data volume and computing power, the performance can be continuously improved. Therefore, in this paper, deep learning is applied to the field of traffic state prediction, which is based on residual network and VGG16 network. The final experimental results show that the classification accuracy of the residual network can reach 92.11%, and the accuracy of the VGG16 network can reach 89.60%, so the residual network is more suitable for the field of traffic congestion detection. © 2024 Copyright held by the owner/author(s).

关键词： Traffic congestion

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LLM for Uniform Information Extraction Using Multi-task Learning Optimization 7th

LLM for Uniform Information Extraction Using Multi-task Lear...

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8th Asia Pacific Web and Web-Age Information Management Joint International conference on Web and Big Data

作者： Li, Ying Tan, Zhen Xiao, Weidong Natl Univ Def Technol Natl Key Lab Informat Syst Engn Kaifu Peoples R China

ISBN: (纸本)9789819600540;9789819600557

With the increasing maturity of deep learning technology, large language models have shown excellent performance in the field of natural language processing, but the performance of information extraction is yet to be further improved. In this paper, we use 7B Llama-2 as a base model training to obtain a large language model capable of using natural language to guide information extraction tasks, which can solve the challenges faced by traditional information extraction methods, and also innovatively use multi-task learning optimization to improve the performance of the large model. We performed large-scale pretraining and instruction tuning on the big model LLama-2. Based on the highquality and richly typed training data automatically constructed by ChatGPT, remote supervision, and other algorithms, which contains a total of 1 million entities, relations, and events, we designed corresponding English templates for instruction tuning. Second, we performed supervised fine-tuning of the model using manually labeled high-quality training sets. To further improve the model performance, we innovatively adopt a multi-task learning optimization strategyGradNorm, which can dynamically adjust the weights of different tasks, thus balancing the losses among tasks during the training process and reducing the overall training loss. After information extraction experiments, our model is compared with other models to test the performance of uniform information extraction for large models, and the experimental results show that our model performs well.

关键词： Large language Model Uniform Information Extraction Instruction Fine-tuning Multi-task Learning Optimization

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BiLD: Bi-directional Logits Difference Loss for Large language Model Distillation 31

BiLD: Bi-directional Logits Difference Loss for Large Langua...

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31st International conference on Computational Linguistics, COLING 2025

作者： Li, Minchong Zhou, Feng Song, Xiaohui KTH Royal Institute of Technology Stockholm Sweden OPPO AI Center Beijing China

ISBN: (纸本)9798891761964

In recent years, large language models (LLMs) have shown exceptional capabilities across various natural language processing (NLP) tasks. However, such impressive performance often comes at the cost of an increased parameter size, posing significant challenges for widespread deployment. To address this issue, knowledge distillation (KD) provides a solution by transferring knowledge from a large teacher model to a smaller student model. In this paper, we explore the task-specific distillation of LLMs at the logit level. Our investigation reveals that the logits of fine-tuned LLMs exhibit a more pronounced long-tail distribution compared to those of vision models, with hidden "noise" in the long tail affecting distillation performance. Furthermore, existing logits distillation methods often struggle to effectively utilize the internal ranking information from the logits. To address these, we propose the Bi-directional Logits Difference (BiLD) loss. The BiLD loss filters out the long-tail noise by utilizing top-k teacher and student logits, and leverages the internal logits ranking information by constructing logits differences. To evaluate BiLD loss, we perform comprehensive experiments on 13 datasets with two types of LLMs. Our results show that the BiLD loss, with only the top-8 logits, outperforms supervised fine-tuning (SFT), vanilla KL loss, and five other distillation methods from both NLP and CV fields. Code is available at https://***/fpcsong/BiLD. © 2025 Association for Computational Linguistics.

关键词： Directional logging

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Emotion Classification Based on Social Media Text Upload Patterns Using the ALBERT Method

Emotion Classification Based on Social Media Text Upload Pat...

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2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Paramartha, Made Rido Maharani, Warih School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Emotion classification in social media texts has several challenges, such as the characteristics of social media texts that tend to use informal language, unbalanced data distribution, overlapping vocabulary between emotion categories. This research explores the ability of the ALBERT model to overcome these challenges by performing data augmentation and hyperparameter tuning and using a dataset of 8,978 tweets labeled with four emotion categories: happy, angry, sad, and fear. This research investigates the impact of hyperparameter tuning and shows a hyperparameter combination that is suitable for the challenges at hand. The hyperparameter combination concerns a learning rate of 1e-5 and batch size of 8 and getting an accuracy value of 89.95% with an F1 Score of 0.8959. The analysis in this research conveyed that the small learning rate tends to have an impact on the ability of the ALBERT model to capture emotional patterns well and in detail. Although ALBERT is considered to be able to handle informal language, it still struggles with unbalanced data distribution and overlapping vocabulary between emotion categories. The results of this study suggest that ALBERT can perform the task of emotion classification in text well and suggest future research to improve by improving preprocessing methods and performing more advanced regulation techniques. © 2025 IEEE.

关键词： ALBERT Emotion Classification Hyperparameter Optimization natural language processing Social Media Analysis

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Transliteration Framework for Tamil-English Code-Mixed Text

Transliteration Framework for Tamil-English Code-Mixed Text

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International conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Viswabharathi, K.A. Rithick, K.R. Thayumanan, T. Nalinadevi, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819777167

Code-mixing is a common phenomenon in multilingual communities, where speakers use more than one language within a single conversation. A transliteration framework is necessary to accommodate individuals who feel more comfortable primarily using their native tongues for communication in the realm of social media. The proposed framework leverages existing transliteration techniques and incorporates novel approaches to handle the challenges specific to English-Tamil code-mixed data. The word-level language classification was implemented using supervised machine learning models and HMM, with the latter performing better with an accuracy of 75%. Tamil-tagged words from the classifier are passed to the Seq2seq encoder-decoder model for transliteration, and the model achieved an accuracy of 93%. The effectiveness of the framework is evaluated using a corpus of code-mixed data, and the results are compared with existing transliteration methods, demonstrating its efficacy in transliterating code-mixed data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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Estimating Priority of Chronology Logs in Earthquakes and Torrential Rains 24

Estimating Priority of Chronology Logs in Earthquakes and To...

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8th International conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Koju, Shinji Takeuchi, Koichi Watanabe, Akihiro Hirayama, Takahiro Nakao, Hiroyuki Graduate School of Environmental Life Natural Science and Technology Okayama University Okayama Japan Faculty of Medicine Hyogo Medical University Hyogo Japan Academic Field of Medicine Dentistry and Pharmaceutical Sciences Okayama University Okayama Japan Department of Physical Therapy Osaka Yukioka College of Health Science Osaka Japan

ISBN: (纸本)9798400717383

The Disaster Response Headquarters receives various requests and reports from affiliated organizations, such as hospitals. Staffs at the headquarters need to prioritize incoming information to effectively allocate limited resources, including supplies and personnel. To organize information chronologically and prioritize responses appropriately, staffs use format called chronology logs to avoid confusion. However, given the large volume of information received, prioritizing entries in the chronology logs is a demanding task. To alleviate this burden, we aim to develop an automatic estimation system for the priority of chronology logs using a deep neural network model. In this study, we classify the priorities in the chronology logs into three levels: high, medium, and low. A dataset annotated manually with these priority levels was created using actual chronology logs to train the model. The chronology logs dataset is imbalanced, with approximately 90% of samples in the low-priority class. In this paper, we compare methods such as weighting, data replication, and data augmentation in training a BERT model on the chronology logs dataset to identify effective techniques to mitigate the adverse effects of class imbalance. We also compare priority estimation models using BERT and GPT embeddings to verify which is most effective for estimating priority of chronology logs. Experimental results show that data replication with BERT achieves the highest performance compared to other methods. © 2024 Copyright held by the owner/author(s).

关键词： text classification

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