FinRED (Financial Relation Extraction)

Figure 1.

Goal

The FinRED dataset is designed for Relation Extraction (RE) in the financial domain. Its primary goals are:

  • To identify and classify semantic relationships between financial entities (e.g., “acquired by”, “subsidiary of”, “competitor of”).

  • To facilitate the construction of financial knowledge graphs.

  • To enable structured information retrieval from unstructured financial texts.

Description

FinRED is a relation extraction dataset from ChanceFocus, consisting of sentence-level relation annotations.

Dataset Composition

  • Source: Financial news and filings.

  • Task: Extract triples in the format (Head Entity, Tail Entity, Relation).

  • Size: 1K - 10K examples (based on HuggingFace metadata).

  • Modality: Text

  • Format: Parquet

Annotation Process

The dataset targets the extraction of specific financial relationships, requiring models to understand the context connecting two entities.

Example

Below is a representative example of the Relation Extraction task:

Table 5 FinRED Examples

ID

Text

Relations (Head; Tail; Rel)

0

Google acquired YouTube in a billion-dollar deal.

Google; YouTube; acquired

1

Apple, based in Cupertino, released the new iPhone.

Apple; Cupertino; headquarters_location

Task Description

Relation Extraction

  • Input: A sentence containing marked entities.

  • Output: A list of triplets head; tail; rel representing the relationships found.

  • Challenge: Handling multiple relations per sentence and identifying the directionality of relationships.

Evaluation Metrics

  1. F1-Score: Harmonic mean of precision and recall for extracted triplets.

  2. Precision: Percentage of extracted relations that are correct.

  3. Recall: Percentage of ground truth relations that were extracted.

Why Use FinRED?

  • Structured Knowledge: Converts unstructured text into structured databases (Knowledge Graphs).

  • Financial Context: capturing specific relations relevant to markets (e.g., M&A, leadership changes).

  • Open Resource: Part of the FLARE benchmark for advancing financial NLP.

References

For dataset access and more details, visit the HuggingFace dataset page.