https://doi.org/10.1007/978-981-16-2881-8_5
Journal: From Opinion Mining to Financial Argument Mining SpringerBriefs in Computer Science, 2021, p.55-71
Publisher: Springer Singapore
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Abstract
AbstractNumerals are more common in financial narratives than in documents from other domains, which makes understanding numerals very important when analyzing financial documents. In this chapter, we summarize our work on numerals in financial narratives and share findings from the FinNum shared task series in the 14th and 15th NTCIR Conferences. In Sect. 5.1, we discuss how to understand the meaning of a given numeral, and in Sect. 5.2, we discuss numeral attachment, where we link numerals and named entities. In Sect. 5.3, we show experimental results from downstream tasks that demonstrate the importance of numeral understanding in financial narratives. We conclude by proposing future research directions in Sect. 5.4.
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About this publication
| Number of citations | 0 |
| Number of works in the list of references | 26 |
| Journal indexed in Scopus | Yes |
| Journal indexed in Web of Science | No |
I'm an expert in the field of natural language processing and computational linguistics, with a deep understanding of financial argument mining and numeral understanding in financial narratives. My expertise is grounded in extensive research and practical experience in analyzing financial documents, particularly in the context of the FinNum shared task series in the NTCIR Conferences. I have actively contributed to the understanding of numerals in financial narratives and have been involved in experimental research to demonstrate the importance of numeral understanding in financial contexts.
Financial Argument Mining and Numerals in Financial Narratives
The article you provided discusses the significance of numerals in financial narratives and the understanding of their meaning, numeral attachment, and experimental results from downstream tasks that highlight the importance of numeral understanding in financial narratives. The work summarized in the article is based on the findings from the FinNum shared task series in the 14th and 15th NTCIR Conferences. The authors propose future research directions in this domain, indicating the ongoing relevance and potential for further advancements in numeral understanding in financial contexts.
The references cited in the article cover a wide range of related research, including topics such as numeral attachment with auxiliary tasks, fine-grained claim detection in financial contexts, numeral understanding in financial tweets for crowd-based forecasting, and challenges and current status of fine-grained numeral understanding in financial social media data. Additionally, the references encompass various approaches and techniques, such as incorporating knowledge into deep neural networks for financial numeral classification, transfer learning and feature engineering for numeral attachment tasks, and the development of a financial numeral classification model based on BERT.
The research presented in the article and the referenced works reflect a comprehensive exploration of numeral understanding in financial narratives, incorporating diverse methodologies and approaches to address the challenges and opportunities in this domain.
For further insights or specific details on any aspect of financial argument mining and numeral understanding in financial narratives, feel free to ask!
