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---
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](#dataset-description)
2. [Supported Tasks](#supported-tasks)
3. [Dataset Structure](#dataset-structure)
4. [Data Fields](#data-fields)
5. [Data Splits](#data-splits)
6. [Dataset Creation](#dataset-creation)
7. [Usage](#usage)
8. [License](#license)
9. [Citation](#citation)
---
### 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
```yaml
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
```python
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
``` bibtex
@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}
}
``` |