README.md

metadata

dataset_info:
  config_name: main
  features:
    - name: context
      dtype: string
    - name: response
      dtype: string
  splits:
    - name: train
      num_bytes: 317988
      num_examples: 2144
    - name: test
      num_bytes: 79011
      num_examples: 537
  download_size: 208114
  dataset_size: 396999
configs:
  - config_name: main
    data_files:
      - split: train
        path: main/train-*
      - split: test
        path: main/test-*

Dataset Card for “NumClaim: Numerical Claim Detection in Finance”

Table of Contents

1. Dataset Description
2. Supported Tasks
3. Dataset Structure
4. Data Fields
5. Data Splits
6. Dataset Creation
7. Usage
8. License
9. Citation

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Dataset Description

NumClaim is a sentence‑level corpus for detecting numerical claims in financial text.
A sentence is labelled as INCLAIM if it expresses a forward‑looking or speculative financial forecast, and OUTOFCLAIM if it states factual past or present information.:contentReference[oaicite:0]{index=0} The dataset combines analyst reports and earnings‑call transcripts, enabling research on the influence of numerical forecasts on market reactions.:contentReference[oaicite:1]{index=1}

---

Supported Tasks

Task	Objective	Typical Metrics
Numerical Claim Classification	Classify a sentence as INCLAIM or OUTOFCLAIM.	Accuracy, Precision, Recall, F1
Optimism Scoring	Produce a continuous optimism score derived from claim likelihood (research use‑case in paper).	Spearman / Pearson correlation with returns

---

Dataset Structure

configs:
  - config_name: main
    data_files:
      - split: train
        path: main/train-*
      - split: test
        path: main/test-*
dataset_info:
  features:
    - context: string   # Financial sentence
    - response: string  # Label: INCLAIM / OUTOFCLAIM
  splits:
    - name: train
      num_examples: 2_144
      num_bytes:    317_988
    - name: test
      num_examples:   537
      num_bytes:     79_011
  download_size: 208_114
  dataset_size: 396_999

Data Fields

Field	Type	Description
context	string	Sentence from an analyst report or earnings call.
response	string	INCLAIM or OUTOFCLAIM label.

---

Data Splits

Split	# Sentences	Portion
Train	2 144	80 %
Test	537	20 %
Total	2 681	100 %

---

Dataset Creation

• Source Collection – Analyst reports (Thomson Reuters) and quarterly earnings‑call transcripts for U.S. public firms (2010 – 2023).
• Sentence Filtering – Retained only sentences containing a financial term, numeric value, and currency/percentage symbol.
• Annotation – Weak‑supervision rules augmented with subject‑matter‑expert knowledge produced initial labels; a subset was manually validated.
• Quality Control – Manual spot‑checks achieved > 0.9 label accuracy, and noisy sentences were removed.

---

Usage

from datasets import load_dataset

numclaim = load_dataset("gtfintechlab/Numclaim")
sample = numclaim["train"][0]

print(sample["context"])
print(sample["response"])

License

Released under Creative Commons Attribution 4.0 International (CC BY 4.0).

Citation

@inproceedings{shah2024numclaim,
  title     = {Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis},
  author    = {Shah, Agam and Hiray, Arnav and Shah, Pratvi and Banerjee, Arkaprabha and Singh, Anushka and Eidnani, Dheeraj and Chava, Sahasra and Chaudhury, Bhaskar and Chava, Sudheer},
  booktitle = {Findings of the Association for Computational Linguistics},
  year      = {2024},
  url       = {https://arxiv.org/abs/2402.11728}
}