short-paper

NumClaim: Investor's Fine-grained Claim Detection

Authors:

Chung-Chi Chen,

Hen-Hsen Huang,

Hsin-Hsi ChenAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 1973 - 1976

https://doi.org/10.1145/3340531.3412100

Published: 19 October 2020 Publication History

Abstract

The goal of claim detection in argument mining is to sort out the key points from a long narrative. In this paper, we design a novel task for argument mining in the financial domain, and provide an expert-annotated dataset, NumClaim, for the proposed task. Based on the statistics, we discuss the differences between the claims in other datasets and the claims of the investors in NumClaim. With the ablation analysis, we show that encoding numeral and co-training with the auxiliary task of the numeral understanding, i.e., the category classification task, can improve the performance of the proposed task under different neural network architectures. The annotations in the NumClaim is published for academic usage under the CC BY-NC-SA 4.0 license.

Supplementary Material

MP4 File (3340531.3412100.mp4)

The goal of claim detection in argument mining is to sort out the key points from a long narrative. In this paper, we design a novel task for argument mining in the financial domain, and provide an expert-annotated dataset, NumClaim, for the proposed task. Based on the statistics, we discuss the differences between the claims in other datasets and the claims of the investors in NumClaim. With the ablation analysis, we show that encoding numeral and co-training with the auxiliary task of the numeral understanding, i.e., the category classification task, can improve the performance of the proposed task under different neural network architectures. The annotations in the NumClaim is published for academic usage under the CC BY-NC-SA 4.0 license.

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References

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Cited By

Shah DShah KJagani MShah AChaudhury B(2024)CONCORD: enhancing COVID-19 research with weak-supervision based numerical claim extractionJournal of Intelligent Information Systems10.1007/s10844-024-00885-6Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10844-024-00885-6
Askari AAbolghasemi APasi GKraaij WVerberne S(2024)Injecting the score of the first-stage retriever as text improves BERT-based re-rankersDiscover Computing10.1007/s10791-024-09435-827:1Online publication date: 26-Jun-2024
https://doi.org/10.1007/s10791-024-09435-8
Shristi Kaur TVerma AKaushal R(2023)Towards Automated Claim Detection In Fact Checking2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308281(1-5)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10308281
Show More Cited By

Index Terms

NumClaim: Investor's Fine-grained Claim Detection
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Published: 19 October 2020

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Ministry of Science and Technology, Taiwan
Academia Sinica, Taiwan

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October 19 - 23, 2020

Virtual Event, Ireland

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Overall Acceptance Rate 520 of 2,712 submissions, 19%

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Cited By

Shah DShah KJagani MShah AChaudhury B(2024)CONCORD: enhancing COVID-19 research with weak-supervision based numerical claim extractionJournal of Intelligent Information Systems10.1007/s10844-024-00885-6Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10844-024-00885-6
Askari AAbolghasemi APasi GKraaij WVerberne S(2024)Injecting the score of the first-stage retriever as text improves BERT-based re-rankersDiscover Computing10.1007/s10791-024-09435-827:1Online publication date: 26-Jun-2024
https://doi.org/10.1007/s10791-024-09435-8
Shristi Kaur TVerma AKaushal R(2023)Towards Automated Claim Detection In Fact Checking2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308281(1-5)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10308281
Askari AAbolghasemi APasi GKraaij WVerberne S(2023)Injecting the BM25 Score as Text Improves BERT-Based Re-rankersAdvances in Information Retrieval10.1007/978-3-031-28244-7_5(66-83)Online publication date: 17-Mar-2023
https://doi.org/10.1007/978-3-031-28244-7_5
Chen CHuang HTakamura HChen H(2022)An Overview of Financial Technology InnovationCompanion Proceedings of the Web Conference 202210.1145/3487553.3524868(572-575)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524868
Liu YLiu MWu M(2022)Numeral Tense Detection in Chinese Financial NewsCompanion Proceedings of the Web Conference 202210.1145/3487553.3524639(604-609)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524639
Ghosh SNaskar S(2022)FiNCAT: Financial Numeral Claim Analysis ToolCompanion Proceedings of the Web Conference 202210.1145/3487553.3524635(583-585)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524635
Ghosh SNaskar S(2022)Detecting context-based in-claim numerals in Financial Earnings Conference CallsInternational Journal of Information Technology10.1007/s41870-022-00952-714:5(2559-2566)Online publication date: 15-May-2022
https://doi.org/10.1007/s41870-022-00952-7
Chen CHuang HChen HDemartini GZuccon GCulpepper JHuang ZTong H(2021)NQuAD: 70,000+ Questions for Machine Comprehension of the Numerals in TextProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482155(2925-2929)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482155
Chen CHuang HHuang YChen HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Distilling Numeral Information for Volatility ForecastingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482089(2920-2924)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482089
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