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Investigating methods to Detect Off-Topic Essays 34th

Investigating Methods to Detect Off-Topic Essays

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34th Brazilian conference on Intelligent Systems

作者： Silva, Joyce M. Anchieta, Rafael T. de Sousa, Rogerio F. Moura, Raimundo S. Fed Inst Piaui Lab Artificial Intelligence Robot & Automat Picos PI Brazil Univ Fed Piaui Lab Nat Language Proc Teresina PI Brazil

ISBN: (纸本)9783031790348;9783031790355

Automated Essay Scoring is one of the most important educational applications of natural language processing. It helps teachers with automatic assessments, providing a cheaper, faster, and more deterministic approach than humans when scoring essays. Nevertheless, off-topic essays pose challenges in this area, causing an automated grader to overestimate the score of an essay that does not adhere to a proposed topic. Thus, detecting off-topic essays is important for dealing with unrelated text responses to a given topic. This paper explored approaches based on handcrafted features to feed supervised machine-learning algorithms, tuning a BERT model, and prompt engineering with a large language model. We assessed these strategies in a public corpus of Portuguese essays, achieving the best result using a fine-tuned BERT model with a 75% balanced accuracy. Furthermore, this strategy was able to identify low-quality essays.

关键词： Automated Essay Scoring Off-Topic Essays BERT

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Reasoning-Oriented and Analogy-Based methods for Locating and Editing in Zero-Shot Event-Relational Reasoning 31

Reasoning-Oriented and Analogy-Based Methods for Locating an...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Tang, Jingyao Li, Lishuang Mi, Liteng Wu, Haiming Lu, Hongbin School of Computer Science and Technology Dalian University of Technology China School of Computer Science and Technology Beijing Institute of Technology China School of Computer Science and Artificial Intelligence Liaoning Normal University China

ISBN: (纸本)9798891761964

Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. However, training prefixes consumes large computational resources and lacks interpretability. Additionally, learning various relational and inferential knowledge inefficiently exploits the connections between tasks. Therefore, we first propose a method for Reasoning-Oriented Locating and Editing (ROLE), which locates and edits the key modules of the language model for reasoning about event relations, enhancing interpretability and also resource-efficiently optimizing the reasoning ability. Subsequently, we propose a method for Analogy-Based Locating and Editing (ABLE), which efficiently exploits the similarities and differences between tasks to optimize the zero-shot reasoning capability. Experimental results show that ROLE improves interpretability and reasoning performance with reduced computational cost. ABLE achieves SOTA results in zero-shot reasoning. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Enhancing Resume Analysis: Leveraging natural language processing and Machine Learning for Automated Resume Screening Using KSA Parameters 5

Enhancing Resume Analysis: Leveraging Natural Language Proce...

引用

5th International conference on Advanced Research in Computing, ICARC 2025

作者： Dilshan, B. A Thisara Asanka, P. P. G Dinesh University of Kelaniya Faculty of Science Department of Industrial Management Sri Lanka

ISBN: (纸本)9798331530983

In the digital age, the recruitment landscape has changed dramatically. This study aims to optimize recruitment by automating resume screening through advanced natural language processing (NLP) and Machine Learning (ML) techniques. The purpose of this research is to address the limitations of traditional Applicant Tracking Systems (ATS) by introducing a framework employing Bidirectional Encoder Representations from Transformers (BERT). A comprehensive methodology was implemented, including data preprocessing, manual dataset annotation, feature extraction via TF-IDF, and cosine similarity for ranking resumes against job descriptions. empirical findings reveal that BERT outperforms other ML technologies including SVM, achieving a classification accuracy of 78.6%, validating its effectiveness in KSA extraction and alignment. Additionally, the system's outputs were validated by industry experts, who confirmed the accuracy and relevance of the extracted KSAs in real-world scenarios. The practical implications of this work include providing actionable insights for HR professionals, enhancing candidate-job matching, and streamlining recruitment workflows. The originality of this study lies in integrating advanced ML models for dynamic KSA classification, paving the way for more efficient recruitment practices. Future research may explore ensemble techniques and transfer learning to further refine the methodology and adapt it to diverse industries. © 2025 IEEE.

关键词： Software design

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Teaching NLP in the Age of Large language Models: Challenges and Strategies for Indian Universities 17th

Teaching NLP in the Age of Large Language Models: Challenges...

引用

17th India Compute conference-COMPUTE-Annual

作者： Madhavan, Manu Kleenankandy, Jeena Indian Inst Informat Technol Kottayam Dept Comp Sci & Engn Kottayam Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi India

ISBN: (纸本)9783031843907;9783031843914

This paper examines the challenges and opportunities in teaching natural language processing (NLP) at the undergraduate level, particularly in the era of Large language Models (LLMs). It discusses the diverse backgrounds of students, including those from computer science and non-computer science disciplines, and the need to incorporate Indian languages into the curriculum. The study identifies key challenges, such as integrating classical NLP methods with contemporary deep learning techniques, and outlines practical issues in syllabus design. By focusing on the unique context of Indian universities, this paper offers actionable recommendations for educators to enhance NLP education. The insights and strategies proposed aim to address the evolving landscape of NLP and prepare students for the demands of the field.

关键词： Teaching NLP Large language Models Curriculum

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Human-like Nonverbal Behavior with MetaHumans in Real-World Interaction Studies: An Architecture Using Generative methods and Motion Capture 20

Human-like Nonverbal Behavior with MetaHumans in Real-World ...

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20th Annual ACM/IEEE International conference on Human-Robot Interaction, HRI 2025

作者： Chojnowski, Oliver Eberhard, Alexander Schiffmann, Michael Müller, Ana Richert, Anja Th Köln - University of Applied Sciences Cologne Cobots Lab Cologne50679 Germany

ISBN: (纸本)9798350378931

Socially interactive agents are gaining prominence in domains like healthcare, education, and service contexts, particularly virtual agents due to their inherent scalability. To facilitate authentic interactions, these systems require verbal and nonverbal communication through e.g., facial expressions and gestures. While natural language processing technologies have rapidly advanced, incorporating human-like nonverbal behavior into real-world interaction contexts is crucial for enhancing the success of communication, yet this area remains underexplored. One barrier is creating autonomous systems with sophisticated conversational abilities that integrate human-like nonverbal behavior. This paper presents a distributed architecture using Epic Games' MetaHuman, combined with advanced conversational AI and camera-based user management, that supports methods like motion capture, handcrafted animation, and generative approaches for nonverbal behavior. We share insights into a system architecture designed to investigate nonverbal behavior in socially interactive agents, deployed in a three-week field study in the Deutsches Museum Bonn, showcasing its potential in realistic nonverbal behavior research. © 2025 IEEE.

关键词： natural language processing systems

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Multi-disease Classification of CT Reports using Traditional natural language processing and a Lightweight Foundation Model

Multi-disease Classification of CT Reports using Traditional...

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2025 conference on Medical Imaging

作者： Garcia-Alcoser, Michael E. Tushar, Fakrul Islam Nejad, Mobina Ghojogh Rubin, Geoffrey D. Lo, Joseph Y. Duke Univ Sch Med Ctr Virtual Imaging Trials Carl E Ravin Adv Imaging LabsDept Radiol Durham NC 27706 USA Univ Arizona Dept Med Imaging Coll Med Tucson AZ USA

ISBN: (纸本)9781510686007;9781510686014

natural language processing (NLP) methods can annotate free-text radiology reports to create large datasets at the scale of an entire health system or beyond. Generalizing the disease classification across multiple organ systems inherently requires a complex, robust, and accurate classification model. Concurrently, NLP methods have significantly improved and become more sophisticated. This study compares two traditional NLP methods, a rule-based algorithm (RBA) and a Bidirectional Long Short-Term Memory network (BiLSTM), with a lightweight variant of the Large language Model Meta AI (Llama) model. Our goal is to analyze the capabilities and limitations of each model in accurately classifying diseases encountered within the chest, abdominal, and pelvic computed tomography (CT) exams of the body. Rule-based algorithms (RBAs) were used to extract disease labels from the "findings" section of CT radiology reports, creating the training, validation, and testing datasets. Disease labels were made for three organ systems: the lungs/pleura, liver/gallbladder, and kidneys/ureters. A BiLSTM network with an attention mechanism was trained on 151,431 cases and tested on 85,987 cases. The BiLSTM and Meta's Llama3.1-8B model was evaluated on the RBA-test set and a manually annotated dataset. On the smaller, manually labeled test set, the RBA model achieved the highest macro F1 score (0.94), followed by the BiLSTM (0.91) and then Llama (0.89). In contrast, on the larger RBA-labeled test set, the BiLSTM maintained high performance (average AUC > 0.98;macro F1 = 0.95), while Llama's macro F1 dropped to 0.65. Manual spot checking of reports where Llama disagreed with RBA/BiLSTM revealed numerous instances in which Llama was actually correct, indicating flaws with the previous RBA labeling. This study emphasizes the limitations of rule-based approaches and the need to consider clinical context in ambiguous scenarios. Llama3.1-8B exhibits the potential to outperform rule-based methods,

关键词： Foundation Model LLM Llama natural language processing BiLSTM Rule-based algorithm Computed Tomography

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Word-level Cross-lingual Structure in Large language Models 31

Word-level Cross-lingual Structure in Large Language Models

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Feng, Zihao Cao, Hailong Xu, Wang Zhao, Tiejun Faculty of Computing Harbin Institute of Technology China

ISBN: (纸本)9798891761964

Large language Models (LLMs) have demonstrated exceptional performance across a broad spectrum of cross-lingual natural language processing (NLP) tasks. However, previous methods predominantly focus on leveraging parallel corpus to conduct instruction data for continuing pre-training or fine-tuning. They ignored the state of parallel data on the hidden layers of LLMs. In this paper, we demonstrate Word-level Cross-lingual Structure (WCS) of LLM which proves that the word-level embedding on the hidden layers are isomorphic between languages. We find that the hidden states of different languages' input on the LLMs hidden layers can be aligned with an orthogonal matrix on word-level. We prove this conclusion in both mathematical and downstream task ways on two representative LLM foundations, LLaMA2 and BLOOM. Besides, we propose an Isomorphism-based Data Augmentation (IDA) method to apply the WCS on a downstream cross-lingual task, Bilingual Lexicon Induction (BLI), in both supervised and unsupervised ways. The experiment shows the significant improvement of our proposed method over all the baselines, especially on low-resource languages. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Forestrank: Automatic Keyphrase Extraction Leveraging Random Forest Classifier and Multi-Criteria Decision-Making 5

Forestrank: Automatic Keyphrase Extraction Leveraging Random...

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5th Asia conference on Information Engineering, ACIE 2025

作者： Singh, Reetika Jain, Goonjan Applied Mathematics Delhi Technological University Delhi India

ISBN: (纸本)9798331528409

Considering the rapidly expanding area of textual data, extracting meaningful insights is a significant challenge. With our novel method for automatic keyphrase extraction, which integrates natural language processing (NLP) and the Random Forest classification model, we offer a unique synthesis of strengths. For the purpose of obtaining clean, meaningful, and labeled noun phrases, we begin by preprocessing documents, utilizing standard NLP techniques, fuzzy strings for similarity measurement, and data labeling. To determine a document's significance, we evaluate multiple feature scores. The Random Forest classifier is used to identify the most relevant features, and Feature Importance scores are calculated using Gini Impurity. Then, we apply these top features to extract keyphrases from noun phrases by using the Weighted Aggregated Sum Product Assessment (WASPAS), MCDM technique. We use noun phrases as alternatives, criteria as the best features, and weights as the Feature Importance scores. Our method is evaluated by measuring accuracy with both the initial set of features and the refined set identified by the Random Forest classifier with a similarity threshold of 0.75. We evaluate our method using SemEval 2017 Task 10, where n counts are 5, 10, and 15 (top nranked predicted keyphrases matched with actual keyphrases). Comparing the results of the selected features to those of all the features, it is evident that the selected features are more accurate. In addition, we compared ForestRank with state-of-the-art methods, and our method outperformed them. © 2025 IEEE.

关键词： natural language processing systems

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Prompt-augmented Feature with Cross-domain Contrastive Learning for Efficient Multi-domain Sentiment Analysis

Prompt-augmented Feature with Cross-domain Contrastive Learn...

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

作者： Li, Rui Yang, Tao Liu, Cheng Jiang, Dazhi Wong, Hau-San Wu, Si Department of Computer Science Shantou University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Engineering South China University of Technology China

ISBN: (纸本)9798350368741

Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more serious in multi-domain scenarios. In this paper, we propose to extract multi-layer features from the PrLM for efficient training since the training process is independent to its large backbone. Meanwhile, compared with the conventional feature extraction, we leverage prompts to induce PrLM for generating sentiment-aware features which lead to significant improvement on the sentiment analysis. In addition, most previous methods adopted a domain alignment paradigm for multi-domain learning, which becomes cumbersome when the number of domains is large. Therefore, we propose a novel prompt-augmented cross-domain contrastive learning for generalizable performance, which clusters samples with the same label under different prompts or domains. Our method is evaluated on two public multi-domain sentiment analysis benchmarks, which significantly outperforms recent state-of-the-art methods. Extensive ablation studies also verify the effectiveness of each proposed component. © 2025 IEEE.

关键词： multi-domain learning pre-trained language model prompt augmentation Sentiment analysis sentiment-aware feature

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MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking

MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracki...

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

作者： Zhang, Chunhui Liu, Li Wen, Hao Zhou, Xi Wang, Yanfeng Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai China Guangzhou China CloudWalk Technology Shanghai China Shanghai AI Laboratory Shanghai China

ISBN: (纸本)9798350368741

Night unmanned aerial vehicle (UAV) tracking is impeded by the challenges of poor illumination, with previous daylight-optimized methods demonstrating suboptimal performance in low-light conditions, limiting the utility of UAV applications. To this end, we propose an efficient mamba-based tracker, leveraging dual enhancement techniques to boost night UAV tracking. The mamba-based low-light enhancer, equipped with an illumination estimator and a damage restorer, achieves global image enhancement while preserving the details and structure of low-light images. Additionally, we advance a cross-modal mamba network to achieve efficient interactive learning between vision and language modalities. Extensive experiments showcase that our method achieves advanced performance and exhibits significantly improved computation and memory efficiency. For instance, our method is 2.8× faster than CiteTracker and reduces 50.2% GPU memory. Our codes are available at https://***/983632847/Awesome-Multimodal-Object-Tracking. © 2025 IEEE.

关键词： language enhancement low-light enhancement mamba network night UAV tracking

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