Agent AI for Finance: From Financial Argument Mining to Agent-Based Modeling
暫譯: 金融領域的代理AI：從金融論證挖掘到基於代理的建模

Chen, Chung-Chi, Takamura, Hiroya

Agent AI for Finance: From Financial Argument Mining to Agent-Based Modeling
  • 出版商: Springer
  • 出版日期: 2025-07-17
  • 售價: $1,540
  • 貴賓價: 9.5 折 $1,463
  • 語言: 英文
  • 頁數: 83
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031946863
  • ISBN-13: 9783031946868
  • 相關分類: Natural Language Processing、Fintech

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This open access book provides an overview of the current state of financial argument mining and financial text generation, and presents the authors' thoughts on the blueprint for NLP in finance in the agent AI era.

Financial documents contain numerous causal inferences and subjective opinions. In a previous book, "From Opinion Mining to Financial Argument Mining" (Springer, 2021), the first author discussed understanding financial documents in a fine-grained manner, particularly those containing opinions. The book highlighted several future directions, such as financial argument mining, multimodal opinion understanding, and analysis generation, and anticipated a lengthy journey for these topics. However, since 2022, ChatGPT and large language models (LLMs) have shown promising advancements, motivating the authors to write this second book on the topic of financial Natural Language Processing (NLP).

Agent-based AI systems have been widely discussed since the advent of LLMs. This book aims to equip researchers and practitioners with the latest methodologies, concepts, and frameworks for developing, deploying, and evaluating AI agents with capabilities in multimodal understanding, decision-making, and interaction. It places a special emphasis on human-centered decision-making and multi-agent cooperation in financial applications. The book surveys the current landscape and discuss future research and development directions.

Targeting a wide audience, from students to seasoned researchers in AI and finance, this book offers an overview of recent trends in Agent AI for finance. It provides a foundation for students to understand the field and design their research direction, while inviting experienced researchers to engage in discussions on open research questions informed by pilot experimental results.

Although this book focuses on financial applications, the discussed concepts and methods can also be applied to other real-world applications by integrating domain-specific characteristics. The authors look forward to seeing new findings and more novel extensions based on the proposed ideas.

商品描述(中文翻譯)

這本開放存取的書籍提供了金融論證挖掘和金融文本生成的現狀概述，並呈現了作者對於在代理人工智慧（Agent AI）時代中金融自然語言處理（NLP）藍圖的思考。

金融文件包含大量的因果推論和主觀意見。在之前的書籍《從意見挖掘到金融論證挖掘》（Springer, 2021）中，第一作者討論了如何以細緻的方式理解金融文件，特別是那些包含意見的文件。該書突出了幾個未來的方向，例如金融論證挖掘、多模態意見理解和分析生成，並預期這些主題將會有漫長的探索之旅。然而，自2022年以來，ChatGPT和大型語言模型（LLMs）顯示出令人鼓舞的進展，促使作者撰寫這本關於金融自然語言處理的第二本書。

自LLMs問世以來，基於代理的人工智慧系統已被廣泛討論。本書旨在為研究人員和實務工作者提供最新的方法論、概念和框架，以開發、部署和評估具備多模態理解、決策和互動能力的人工智慧代理。它特別強調以人為中心的決策和金融應用中的多代理合作。本書調查了當前的現狀並討論未來的研究和發展方向。

本書面向廣泛的讀者群，從學生到資深的人工智慧和金融研究人員，提供了金融領域中代理人工智慧的最新趨勢概述。它為學生理解該領域和設計研究方向提供了基礎，同時邀請經驗豐富的研究人員參與基於初步實驗結果的開放研究問題討論。

儘管本書專注於金融應用，但所討論的概念和方法也可以通過整合特定領域的特徵應用於其他現實世界的應用。作者期待看到基於所提出的想法的新發現和更具創新性的擴展。

作者簡介

Chung-Chi Chen is currently a researcher at the Artificial Intelligence Research Center, AIST, Japan. His scholarly pursuits revolve around the intricate realm of financial opinion mining and the nuanced understanding and generation of financial documents. He is the founder of ACL SIG-FinTech, and he has orchestrated the FinNLP/FinWeb workshop series within prestigious conferences such as IJCAI, WWW, EMNLP, and IJCNLP-AACL since 2019. He has guided the FinNum and FinArg shared task series on the NTCIR since 2018. He was also a presenter in the AACL-2020, EMNLP-2021, ECAI-2024, and SIGIR-2025 tutorials. He served as a program co-chair of NTCIR-18, senior area chair of ACL-2024, and PC member in many representative conferences. In academic competitions, he won the SIGIR Early Career Researcher Award (Excellence in Community Engagement), in addition to two Thesis Awards and Technology Innovation Award. Outside academia, he has actively explored the fast-paced FinTech industry, winning multiple awards in startup, FinTech, and LegalTech competitions.

Hiroya Takamura is the research team leader at Knowledge and Information Research Team at the Artificial Intelligence Research Center, AIST, Japan. His research interest includes sentiment analysis, text summarization, and natural language generation. He has authored a number of papers regarding with knowledge extraction for numerical attributes, and understanding and generating numbers in text. In addition, he has experience in organizing conferences and workshops. He was a member of the organization committee of AACL. He served as a program chair of International Conference on Natural Language Generation (INLG) 2019. He has also organized several workshops. He served as an area chair of some conferences (EMNLP, EACL, COLING) and as a PC member of many conferences (ACL, NAACL, EMNLP, EACL, COLING, and so on).

作者簡介(中文翻譯)

陳忠志目前是日本產業技術綜合研究所（AIST）人工智慧研究中心的研究員。他的學術研究圍繞著金融意見挖掘的複雜領域，以及對金融文件的細緻理解與生成。他是ACL SIG-FinTech的創始人，自2019年以來，他在IJCAI、WWW、EMNLP和IJCNLP-AACL等知名會議中策劃了FinNLP/FinWeb工作坊系列。他自2018年以來指導了NTCIR的FinNum和FinArg共享任務系列。他還在AACL-2020、EMNLP-2021、ECAI-2024和SIGIR-2025的教程中擔任講者。他曾擔任NTCIR-18的程式共同主席、ACL-2024的高級區域主席，以及多個代表性會議的程序委員會成員。在學術競賽中，他獲得了SIGIR早期職業研究者獎（社區參與卓越獎），以及兩個論文獎和技術創新獎。在學術界之外，他積極探索快速發展的金融科技產業，並在創業、金融科技和法律科技競賽中贏得多項獎項。

高村浩也是日本產業技術綜合研究所（AIST）人工智慧研究中心知識與資訊研究團隊的研究團隊負責人。他的研究興趣包括情感分析、文本摘要和自然語言生成。他撰寫了多篇有關數值屬性知識提取，以及理解和生成文本中數字的論文。此外，他在組織會議和工作坊方面也有經驗。他曾是AACL的組織委員會成員，並擔任2019年國際自然語言生成會議（INLG）的程式主席。他還組織了幾個工作坊，並擔任過一些會議（EMNLP、EACL、COLING）的區域主席，以及多個會議（ACL、NAACL、EMNLP、EACL、COLING等）的程序委員會成員。

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