@inproceedings{liu-etal-2018-riskfinder,
title = "{R}isk{F}inder: A Sentence-level Risk Detector for Financial Reports",
author = "Liu, Yu-Wen and
Liu, Liang-Chih and
Wang, Chuan-Ju and
Tsai, Ming-Feng",
editor = "Liu, Yang and
Paek, Tim and
Patwardhan, Manasi",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-5017",
doi = "10.18653/v1/N18-5017",
pages = "81--85",
abstract = "This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at \url{https://cfda.csie.org/RiskFinder/}.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liu-etal-2018-riskfinder">
<titleInfo>
<title>RiskFinder: A Sentence-level Risk Detector for Financial Reports</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yu-Wen</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liang-Chih</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chuan-Ju</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ming-Feng</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tim</namePart>
<namePart type="family">Paek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manasi</namePart>
<namePart type="family">Patwardhan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Orleans, Louisiana</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at https://cfda.csie.org/RiskFinder/.</abstract>
<identifier type="citekey">liu-etal-2018-riskfinder</identifier>
<identifier type="doi">10.18653/v1/N18-5017</identifier>
<location>
<url>https://aclanthology.org/N18-5017</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>81</start>
<end>85</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T RiskFinder: A Sentence-level Risk Detector for Financial Reports
%A Liu, Yu-Wen
%A Liu, Liang-Chih
%A Wang, Chuan-Ju
%A Tsai, Ming-Feng
%Y Liu, Yang
%Y Paek, Tim
%Y Patwardhan, Manasi
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F liu-etal-2018-riskfinder
%X This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at https://cfda.csie.org/RiskFinder/.
%R 10.18653/v1/N18-5017
%U https://aclanthology.org/N18-5017
%U https://doi.org/10.18653/v1/N18-5017
%P 81-85
Markdown (Informal)
[RiskFinder: A Sentence-level Risk Detector for Financial Reports](https://aclanthology.org/N18-5017) (Liu et al., NAACL 2018)
ACL
- Yu-Wen Liu, Liang-Chih Liu, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. RiskFinder: A Sentence-level Risk Detector for Financial Reports. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 81–85, New Orleans, Louisiana. Association for Computational Linguistics.