Identifying Corporate Credit Risk Sentiments from Financial News (2022.naacl-industry)
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| Challenge: | Existing methods to assess default probabilities are tedious and time-consuming due to the deluge of news coverage for financial institutions. |
| Approach: | They propose a deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company. |
| Outcome: | The proposed system leverages news extraction and data enrichment with targeted sentiment entity recognition to detect companies and text classification to identify credit events. |
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| Challenge: | Credit risk monitoring is an essential process for financial institutions to evaluate the creditworthiness of borrowing entities. |
| Approach: | They propose a system which proactively alerts Credit Officers to credit-relevant news events . the system has been deployed for nearly three years and has an estimated precision of 77% . |
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Financial Risk Relation Identification through Dual-view Adaptation (2025.emnlp-main)
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| Challenge: | Identifying inter-firm risk relations is crucial for applications like portfolio management and investment strategy. |
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DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)
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| Challenge: | Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system. |
| Approach: | They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news. |
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FIRE: A Dataset for Financial Relation Extraction (2024.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) datasets require extensive linguistic and domain knowledge, making dataset creation costly and labor-intensive. |
| Approach: | They introduce a sentence-level dataset of named entities and relations within the financial sector that encapsulates 13 named entity types along with 18 relation types. |
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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
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A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News (2020.lrec-1)
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| Challenge: | Strict regulatory regimes mandate financial institutions to rigorously monitor their customers' financial activities. |
| Approach: | They propose to use an ontology of compliance-related concepts and relationships along with a corpus annotated according to it to train and evaluate named entity recognition algorithms. |
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Forecasting Firm Material Events from 8-K Reports (D19-51)
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| Challenge: | In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents of the company’s 8-K Current Reports. |
| Approach: | They exploit state-of-the-art neural architectures, including sequence-to-sequence architecture and attention mechanisms, to build a deep learning model that can forecast firm material event sequences based on company 8-K Current Reports. |
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Event-Driven Learning of Systematic Behaviours in Stock Markets (2020.findings-emnlp)
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| Challenge: | Using financial news, we can predict stock market behaviours by extracting financial events from the news and ranking the importance of the events. |
| Approach: | They propose to combine open information extraction and neural co-reference resolution to extract financial events from news streams and extend hierarchical attention networks that include attentions on event, news and temporal levels. |
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Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation (2025.acl-srw)
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| Challenge: | Existing datasets that are outdated and inaccurate hinder accuracy of Financial Sentiment Analysis (FSA) . |
| Approach: | They propose a data augmentation technique using Retrieval Augmented Generation (RAG) to infuse established benchmarks with up-to-date contextual information from contemporary financial news. |
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FinEntity: Entity-level Sentiment Classification for Financial Texts (2023.emnlp-main)
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| Challenge: | FinEntity annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
| Approach: | They introduce an entity-level sentiment classification dataset called FinEntity that annotates financial entity spans and their sentiment in financial news. |
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