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Proceedings - ALPIT 2008, 7th international conference on advanced language processing and web information technology

Proceedings - ALPIT 2008, 7th International Conference on Ad...

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ALPIT 2008, 7th international conference on advanced language processing and web information technology

ISBN: (纸本)9780769532738

the proceedings contain 101 papers. the topics discussed include: exploring various features in semantic role labeling;transformation rule learning without rule templates: a case study in part of speech tagging;word sense disambiguation based on relation structure;Chinese sentence similarity measure based on words and structure information;email classification using semantic feature space;a clustering approach of conceptual sentence groups;deriving a semantic classification tree of Korean verbs based on semantic features;Korean syntactic analysis using dependency rules and segmentation;tree kernel-based semantic relation extraction using unified dynamic relation tree;near-duplicates detection for Vietnamese documents in large database;latent semantic kernels for wordnet: transforming a tree-like structure into a matrix;and a new feature-fusion sentence selecting strategy for query-focused multi-document summarization.

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Pre-Trained Encoder Decoder Transformer Model for language processing 6

Pre-Trained Encoder Decoder Transformer Model for Language P...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Farhan Akthar, K. Deepaksakthi, V.K. Tamizhselvi, A. Department of Information Technology St.Joseph’s College of Engineering Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Tamil language processing in NLP has yet to be outstanding, mainly because of the absence of high-quality resources. In this project, a novel approach to address these limitations is to build an encoder-decoder transformer model specifically for the Tamil language. the comprehensive Tamil text dataset is augmented along with transformer architectures to further enhance the performance on tasks such as machine translation, text summarization, and generation. Character-level processing further allows Tamil to some extent to capture subtleties, while the multi-head self-attention mechanisms bring in a new dimension to unveil complex linguistic dependencies of the text. Preliminary results look promising for gaining this efficiency in language processing as it outperformed prevailing approaches in key Tamil NLP tasks. Using the Transformer model for processing the Tamil language holds potential for filling an important gap for underrepresented languages in NLP, and further contributes to developing more inclusive AI. © 2025 IEEE.

关键词： Decoding

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Joint Workshop of the 9th Financial technology and Natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025

Joint Workshop of the 9th Financial Technology and Natural L...

引用

Joint Workshop of the 9th Financial technology and Natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

ISBN: (纸本)9798891762091

the proceedings contain 48 papers. the topics discussed include: Chat Bankman-Fried: an exploration of LLM alignment in finance;GraphRAG analysis for financial narrative summarization and a framework for optimizing domain adaptation;BuDDIE: a business document dataset for multi-task information extraction;FinMoE: a MoE-based large Chinese financial language model;bridging the gap: efficient cross-lingual NER in low-resource financial domain;evaluating financial literacy of large language models through domain specific languages for plain text accounting;synthetic data generation using large language models for financial question answering;training LayoutLM from scratch for efficient named-entity recognition in the insurance domain;and investigating the effectiveness of length based rewards in DPO for building conversational financial question answering systems.

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Classification of Emoji in Text Documents of Users in Social Networks using Machine Learning 6

Classification of Emoji in Text Documents of Users in Social...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Muhamediyeva, Dildora Niyozmatova, Nilufar Turgunova, Nafisakhon Ungalov, Sanjar Almuradova, Nigora Department of Information Technology Software Tashkent University of Information Technologies named after Mukhammad al-Khwarizmi Tashkent city Uzbekistan Dept. Digital Technologies and Artificial Intelligence Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent city Uzbekistan

ISBN: (纸本)9798331522667

this paper presents a study conducted to classify emotions in social media texts using machine learning. With the rapid increase in the amount of data in digital media, efficient and highly accurate methods for analyzing the emotional content of texts are needed. It is shown that modern deep learning algorithms, natural language processing methods, and feature extraction methods work well for emotion analysis tasks. Data preprocessing methods such as lemmatization, normalization, and text cleaning are given special attention since they are crucial to improving the quality of analysis. the paper also discusses the performance metrics such as precision, recall, and F1-score to demonstrate the advantages of the proposed methods. Furthermore, the current challenges in processing unstructured social media data such as user review analysis and social trend detection are discussed. By leveraging multimodal data and modern context-aware models, the work places special emphasis on improving emotion classification. It is important to leverage advanced machine learning methods to improve text analysis in the dynamic context of social media. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Predictive Process Monitoring Using Semantic information

Enhancing Predictive Process Monitoring Using Semantic Infor...

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international Workshops which were held in conjunction with the 6th international conference on Process Mining, ICPM 2024

作者： Yuan, Jiaxin Grigori, Daniela van der Aa, Han Paris Dauphine University-PSL Pl. du Maréchal de Lattre de Tassigny Paris75016 France University of Vienna Universitätsring 1 Wien1010 Austria

ISBN: (纸本)9783031822247

Predictive Process Monitoring (PPM) leverages historical data to forecast information about ongoing business processes. Recent methods have utilized advanced deep learning and classical machine learning models. However, the role of semantic information that can be extracted from event logs has been underexplored, although such information has been demonstrated to have significant advantages for other process mining tasks, such as anomaly detection. therefore, this paper proposes a novel mechanism that aims to exploit semantic information for PPM, particularly by extracting information regarding the status of business objects associated with process instances from event data. We evaluate this mechanism in outcome-oriented and next activity prediction tasks, using state-of-the-art large language models (LLMs) for semantic extraction. Our results show that integrating semantic information improves prediction performance across these tasks. this work demonstrates that utilizing semantic information in PPM has considerable potential, especially in combination with advanced language models. © the Author(s) 2025.

关键词： Prediction models

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Generating Financial News Articles from Factors of Stock Price Rise / Decline by LLMs 9

Generating Financial News Articles from Factors of Stock Pri...

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作者： Nishida, Shunsuke Utsuro, Takehito Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan

ISBN: (纸本)9798891762091

In this paper, we study the task of generating financial news articles related to stock price fluctuations. Traditionally, reporters manually write these articles by identifying the causes behind significant stock price volatility. However, this process is time-consuming, limiting the number of articles produced. To address this, the study explores the use of generative AI to automatically generate such articles. the AI system, similar to human reporters, would analyze stock price volatility and determine the underlying factors contributing to these fluctuations. To support this approach, we introduces a Japanese dataset called JFinSR, which includes stock price fluctuation rankings from "Kabutan" and related financial information regarding factors of stock price rise / decline from "Nihon Keizai Shimbun (Nikkei)." Using this dataset, we implement the few-shot learning technique on large language models (LLMs) to enable automatic generation of high-quality articles from factors of stock price rise / decline that are available in Nikkei. In the evaluation, we compare zero-shot and few-shot learning approaches, where the few-shot learning achieved the higher F1 scores in terms of ROUGE-1/ROUGE-L metrics. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Construction of an Online Autonomous Learning Model Based on Artificial Intelligence ChatGPT

Construction of an Online Autonomous Learning Model Based on...

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6th international conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Li, Aimin Yang, Chenming Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9783031882869

As information technology advances swiftly, AI is increasingly deployed across numerous sectors, especially in the education industry. In order to make up for the shortcomings of traditional education, online learning has become an emerging way of learning, and there are various online learning platforms, however, the existing online learning system cannot satisfy the individualized learning requirements of learners., thus making the learning effect poor. this paper proposes an online independent learning model based on ChatGPT technology, which offers tailored learning materials to students and enhances their learning outcomes by leveraging natural language processing and AI-driven data analytics. ChatGPT’s advanced data processing capability can integrate students’ learning resources and adapt the learning journey in real-time based on students’ behaviors to offer bespoke educational trajectories. And can be used for learning based on students’ learning behaviours. ChatGPT’s advanced data processing capability can integrate students’ learning resources and modify the learning pathway in response to students’ interaction patterns., providing students with personalized learning paths, customising learning plans according to students’ needs to increase their interest in learning. the effectiveness of the new model has been verified through experiments, and the model can markedly enhance students’ academic achievements and enthusiasm for learning, and the results of the satisfaction survey show that students have a high degree of satisfaction with the model, especially the personalised recommendation function and the instant feedback function of the model have been highly evaluated. the model is still in the preliminary experimental stage, and the next step will be to optimise the model algorithm to further improve the performance of the model and provide assistance for the development of educational technology. © the Author(s), under exclusive license to Springer Nat

关键词： Natural language processing systems

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MLEE: Event Extraction as Multi-label Classification Task at Token Level 6th

MLEE: Event Extraction as Multi-label Classification Task a...

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6th IFIP TC 12 international conference on Intelligence Science, ICIS 2024

作者： Yang, Jinshun Huang, Shuangxi Huang, Mingfeng Tsinghua University Beijing China

ISBN: (纸本)9783031712524

Event Extraction is an important task in natural language understanding, which aims to identify event trigger of pre-defined event types and their arguments of specific roles, has attracted a lot of attention from industry and academia. the previous works failed to address some issues, including error propagation problem, overlap and nest problem, and high complexity of model. this work proposes a novel model MLEE, which models Event Extraction task as Multi-Label classification task at token level, and processes the extraction task in a joint paradigm, can help solving issues mentioned above. the experiment verifies our model’s effectiveness. Empirical results on DuEE and FewFC shows that MLEE outperforms previous best model, pushing trigger extraction F1 to 85.03% (+4.45%), argument extraction F1 to 78.85% (+2.72%) on DuEE, pushing trigger extraction F1 to 76.69% (+1.63%), argument extraction F1 to 76.53% (+5.27%) on FewFC. © IFIP international Federation for information processing 2025.

关键词： Natural language processing systems

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Modeling Interactions Between Stocks Using LLM-Enhanced Graphs for Volume Prediction 9

Modeling Interactions Between Stocks Using LLM-Enhanced Grap...

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作者： Xu, Zhiyu Liu, Yi Wang, Yuchi Bao, Ruihan Harimoto, Keiko Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Mizuho Securities Co. Ltd. Japan

ISBN: (纸本)9798891762091

Accurate trading volume prediction is essential for portfolio optimization, market regulation, and financial risk control. An effective method for predicting trading volume involves building a graph to model relations between stocks. Recent research has enhanced these models by integrating stock news to improve forecasting ability. However, existing approaches primarily integrate news data as auxiliary features for nodes in Graph Neural Networks (GNNs), often overlooking the relational information between stocks embedded in news. To address this, we propose LLM-Enhanced Dynamic Graph Neural Network (LED-GNN), a framework that constructs dynamic graphs using inter-stock relationships extracted from news via a large language model (LLM)-centered pipeline. the news graph is then combined with graphs learned from historical price-volume data and fed into a dynamic GNN to generate predictions. Evaluated on a real-world dataset, TOPIX, with Reuters Financial News, LED-GNN consistently outperformed all baseline models, achieving a 2% improvement over the strongest baseline. © 2025 Association for Computational Linguistics.

关键词： Prediction models

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BuDDIE: A Business Document Dataset for Multi-task information Extraction 9

BuDDIE: A Business Document Dataset for Multi-task Informati...

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作者： Wang, Dongsheng Zmigrod, Ran Sibue, Mathieu Pei, Yulong Babkin, Petr Brugere, Ivan Liu, Xiaomo Navarro, Nacho Papadimitriou, Antony Watson, William Ma, Zhiqiang Nourbakhsh, Armineh Shah, Sameena JPMorgan AI Research United States

ISBN: (纸本)9798891762091

the field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in the multi-modal domain. Several datasets exist for research on specific tasks of VRDU, such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia. these datasets cover documents like invoices and receipts with sparse annotations such that they support one or two co-related tasks (e.g., entity extraction and entity linking). Unfortunately, only focusing on a single specific type of documents or task is not representative of how documents often need to be processed in the wild – where variety in style and requirements is expected. In this paper, we introduce BuDDIE (Business Document Dataset for information Extraction),1 the first multi-task dataset of 1,665 real-world business documents that contains rich and dense annotations for DC, KEE, and VQA. Our dataset consists of publicly available business entity documents from US state government websites. the documents are structured and vary in their style and layout across states and types (e.g., forms, certificates, reports, etc.). We provide data variety and quality metrics for BuDDIE as well as a series of baselines for each task. Our baselines cover traditional textual, multimodal, and large language model approaches to VRDU. © 2025 Association for Computational Linguistics.

关键词： information retrieval

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