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        <title>Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network</title>
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        <namePart type="given">Yu-Chi</namePart>
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    <name type="personal">
        <namePart type="given">Min-Chen</namePart>
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    <name type="personal">
        <namePart type="given">Wen-Chao</namePart>
        <namePart type="family">Yeh</namePart>
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    <name type="personal">
        <namePart type="given">Yung-Chun</namePart>
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            <namePart type="given">Chia-Hui</namePart>
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            <namePart type="given">Kuan-Yu</namePart>
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    <abstract>Machine learning methods for financial document analysis have been focusing mainly on the textual part. However, the numerical parts of these documents are also rich in information content. In order to further analyze the financial text, we should assay the numeric information in depth. In light of this, the purpose of this research is to identify the linking between the target cashtag and the target numeral in financial tweets, which is more challenging than analyzing news and official documents. In this research, we developed a multi model fusion approach which integrates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). We also encode dependency information behind text into the model to derive semantic latent features. The experimental results show that our model can achieve remarkable performance and outperform comparisons.</abstract>
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%0 Conference Proceedings
%T Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network
%A Liang, Yu-Chi
%A Chen, Min-Chen
%A Yeh, Wen-Chao
%A Chang, Yung-Chun
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F liang-etal-2021-numerical
%X Machine learning methods for financial document analysis have been focusing mainly on the textual part. However, the numerical parts of these documents are also rich in information content. In order to further analyze the financial text, we should assay the numeric information in depth. In light of this, the purpose of this research is to identify the linking between the target cashtag and the target numeral in financial tweets, which is more challenging than analyzing news and official documents. In this research, we developed a multi model fusion approach which integrates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). We also encode dependency information behind text into the model to derive semantic latent features. The experimental results show that our model can achieve remarkable performance and outperform comparisons.
%U https://aclanthology.org/2021.rocling-1.28
%P 218-225

Markdown (Informal)

[Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network](https://aclanthology.org/2021.rocling-1.28) (Liang et al., ROCLING 2021)

• Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network (Liang et al., ROCLING 2021)

ACL

• Yu-Chi Liang, Min-Chen Chen, Wen-Chao Yeh, and Yung-Chun Chang. 2021. Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 218–225, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).