Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)最新文献_第3页

DCU-ML at the FinNLP-2022 ERAI Task: Investigating the Transferability of Sentiment Analysis Data for Evaluating Rationales of Investors DCU-ML在FinNLP-2022 ERAI任务:调查情绪分析数据的可转移性，以评估投资者的基本原理

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.finnlp-1.14

Chenyang Lyu, Tianbo Ji, Liting Zhou

{"title":"DCU-ML at the FinNLP-2022 ERAI Task: Investigating the Transferability of Sentiment Analysis Data for Evaluating Rationales of Investors","authors":"Chenyang Lyu, Tianbo Ji, Liting Zhou","doi":"10.18653/v1/2022.finnlp-1.14","DOIUrl":"https://doi.org/10.18653/v1/2022.finnlp-1.14","url":null,"abstract":"In this paper, we describe our system for the FinNLP-2022 shared task: Evaluating the Rationales of Amateur Investors (ERAI). The ERAI shared tasks focuses on mining profitable information from financial texts by predicting the possible Maximal Potential Profit (MPP) and Maximal Loss (ML) based on the posts from amateur investors. There are two sub-tasks in ERAI: Pairwise Comparison and Unsupervised Rank, both target on the prediction of MPP and ML. To tackle the two tasks, we frame this task as a text-pair classification task where the input consists of two documents and the output is the label of whether the first document will lead to higher MPP or lower ML. Specifically, we propose to take advantage of the transferability of Sentiment Analysis data with an assumption that a more positive text will lead to higher MPP or higher ML to facilitate the prediction of MPP and ML. In experiment on the ERAI blind test set, our systems trained on Sentiment Analysis data and ERAI training data ranked 1st and 8th in ML and MPP pairwise comparison respectively. Code available in this link.","PeriodicalId":331851,"journal":{"name":"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134337730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Ranking Environment, Social And Governance Related Concepts And Assessing Sustainability Aspect of Financial Texts 对环境、社会和治理相关概念进行排名，并评估金融文本的可持续性方面

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.finnlp-1.33

Sohom Ghosh, S. Naskar

引用次数: 2

A Taxonomical NLP Blueprint to Support Financial Decision Making through Information-Centred Interactions 通过以信息为中心的交互支持财务决策的分类NLP蓝图

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.finnlp-1.10

Siavash Kazemian, Cosmin Munteanu, Gerald Penn

{"title":"A Taxonomical NLP Blueprint to Support Financial Decision Making through Information-Centred Interactions","authors":"Siavash Kazemian, Cosmin Munteanu, Gerald Penn","doi":"10.18653/v1/2022.finnlp-1.10","DOIUrl":"https://doi.org/10.18653/v1/2022.finnlp-1.10","url":null,"abstract":"Investment management professionals (IMPs) often make decisions after manual analysis of text transcripts of central banks’ conferences or companies’ earning calls. Their current software tools, while interactive, largely leave users unassisted in using these transcripts. A key component to designing speech and NLP techniques for this community is to qualitatively characterize their perceptions of AI as well as their legitimate needs so as to (1) better apply existing NLP methods, (2) direct future research and (3) correct IMPs’ perceptions of what AI is capable of. This paper presents such a study, through a contextual inquiry with eleven IMPs, uncovering their information practices when using such transcripts. We then propose a taxonomy of user requirements and usability criteria to support IMP decision making, and validate the taxonomy through participatory design workshops with four IMPs. Our investigation suggests that: (1) IMPs view visualization methods and natural language processing algorithms primarily as time-saving tools that are incapable of enhancing either discovery or interpretation and (2) their existing software falls well short of the state of the art in both visualization and NLP.","PeriodicalId":331851,"journal":{"name":"Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123513717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

It’s Time to Reason: Annotating Argumentation Structures in Financial Earnings Calls: The FinArg Dataset 是时候推理了:在财务财报电话会议中注释论证结构:FinArg数据集

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.finnlp-1.22

Alaa Alhamzeh, Romain Fonck, Erwan Versmée, Elöd Egyed-Zsigmond, H. Kosch, L. Brunie

引用次数: 1

Automatic Term and Sentence Classification Via Augmented Term and Pre-trained language model in ESG Taxonomy texts 基于扩充词和预训练语言模型的ESG分类文本术语和句子自动分类

Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.finnlp-1.30

Ke Tian, Zepeng Zhang, Hua Chen

引用次数: 0