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| Titre : |
Agent AI for Finance : From Financial Argument Mining to Agent-Based Modeling |
| Type de document : |
document électronique |
| Auteurs : |
Chung Chi Chen, Auteur ; Hiroya Takamura, Auteur |
| Editeur : |
Berlin [Germany] : Springer Nature Limited |
| Année de publication : |
2025 |
| Importance : |
83 p. |
| Présentation : |
ill., couv. ill. |
| ISBN/ISSN/EAN : |
978-3-031-94687-5 |
| Langues : |
Anglais (eng) |
| Catégories : |
Open Access Publications
|
| Tags : |
Artificial Intelligence (AI) Multiagent Systems Machine Learning Computer Science Business Information Systems, Natural Language Processing (NLP) |
| Index. décimale : |
006.3 Intelligence artificielle |
| Résumé : |
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.
|
| En ligne : |
https://doi.org/10.1007/978-3-031-94687-5 |
Agent AI for Finance : From Financial Argument Mining to Agent-Based Modeling [document électronique] / Chung Chi Chen, Auteur ; Hiroya Takamura, Auteur . - Berlin (Germany) : Springer Nature Limited, 2025 . - 83 p. : ill., couv. ill. ISBN : 978-3-031-94687-5 Langues : Anglais ( eng)
| Catégories : |
Open Access Publications
|
| Tags : |
Artificial Intelligence (AI) Multiagent Systems Machine Learning Computer Science Business Information Systems, Natural Language Processing (NLP) |
| Index. décimale : |
006.3 Intelligence artificielle |
| Résumé : |
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.
|
| En ligne : |
https://doi.org/10.1007/978-3-031-94687-5 |
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Agent AI for Finance
