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Ranking Financial Relationships.pdf

2017_repke_comparing_2.pdf
Preview of Ranking Financial Relationships
🔗 Source: hpi.de
📊 Size: 389 KB
👤 Author: Tim Repke, Michael Loster, and Ralf Krestel
⬇️ Downloads: 205

Summary

Evaluating company credibility is complex, requiring analysis of large amounts of unstructured data from various sources. Automatically extracting relationships between financial entities from text reduces manual work, but not all relationships are important. This paper proposes ranking extracted relationships based on text snippets to display important information prominently. A dataset of triples extracted from 10-K and 10-Q filings is used, with text snippets providing context for relationships labeled by experts as irrelevant, neutral, relevant, or highly relevant. The approach uses multi-class classifiers trained on experts' labels, comparing three feature sets: bag-of-words, embeddings, and syntax features. Experimental results show that an ensemble of the three models achieves the best performance, with a mean NDCG score of 0.95.

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Description

Evaluating company credibility is complex, requiring analysis of large amounts of unstructured data from various sources.

Technical Information

  • File Format: PDF
  • File Size: 389 KB
  • Pages: 2
  • Language: EN
  • Author: Tim Repke, Michael Loster, and Ralf Krestel
  • Total Downloads: 205
  • Last Updated: 6 days ago

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