FinSense: An Assistant System for Financial Journalists and Investors

Liou, Yi-Ting; Chen, Chung-Chi; Tang, Tsun-Hsien; Huang, Hen-Hsen; Chen, Hsin-Hsi

doi:10.1145/3437963.3441704

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…In our previous work (Liou et al, 2021), we apply the proposed task to accelerate the working process of the journalists and show that using the extracted entities could be useful for downstream tasks such as news aggregation and stock movement prediction. In the future, we plan to apply the proposed approach to datasets with both graphical knowledge and textual content.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Graph Transformer for Implicit Tag Recognition

Liou¹,

Chen²,

Huang³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Textual information extraction is a typical research topic in the NLP community. Several NLP tasks such as named entity recognition and relation extraction between entities have been well-studied in previous work. However, few works pay their attention to the implicit information. For example, a financial news article mentioned "Apple Inc." may be also related to Samsung, even though Samsung is not explicitly mentioned in this article. This work presents a novel dynamic graph transformer that distills the textual information and the entity relations on the fly. Experimental results confirm the effectiveness of our approach to implicit tag recognition.

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Section: Discussionmentioning

confidence: 99%

Dynamic Graph Transformer for Implicit Tag Recognition

Liou¹,

Chen²,

Huang³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Self Cite

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“…A homogeneous graph is a graph with one type of node and one type of edge. For instance, Liou et al (2021) construct a financial news co-occurrence graph, where two companies are connected if they are tagged in the same news articles. Li et al (2020b) build a transaction network, where nodes are accounts and are connected when there exist transactions between them.…”

Section: Homogeneous Graphmentioning

confidence: 99%

“…Taking the contexts into account, language models achieve the state of art performance on many NLP tasks and are widely used in the literature. For instance, Liou et al (2021) use bidirectional encoder representations from transformers (BERT) model (Devlin et al, 2019) to encode the entire news article and generated a news embedding. Without modifying the model architecture, the pretrained BERT model is able to be fine-tuned and produce state-of-art performance.…”

Section: Language Modelmentioning

confidence: 99%

A Review on Graph Neural Network Methods in Financial Applications

Wang¹,

Zhang²,

Song³

2021

Preprint

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Keeping the individual features and the complicated relations, graph data are widely utilized and investigated. Being able to capture the structural information by updating and aggregating nodes' representations, graph neural network (GNN) models are gaining popularity. In the financial context, the graph is constructed based on real-world data, which leads to complex graph structure and thus requires sophisticated methodology. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.

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