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--- |
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dataset_info: |
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config_name: main |
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features: |
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- name: context |
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dtype: string |
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- name: response |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 317988 |
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num_examples: 2144 |
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- name: test |
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num_bytes: 79011 |
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num_examples: 537 |
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download_size: 208114 |
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dataset_size: 396999 |
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configs: |
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- config_name: main |
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data_files: |
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- split: train |
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path: main/train-* |
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- split: test |
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path: main/test-* |
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--- |
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## Dataset Card for “NumClaim: Numerical Claim Detection in Finance” |
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### Table of Contents |
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1. [Dataset Description](#dataset-description) |
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2. [Supported Tasks](#supported-tasks) |
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3. [Dataset Structure](#dataset-structure) |
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4. [Data Fields](#data-fields) |
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5. [Data Splits](#data-splits) |
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6. [Dataset Creation](#dataset-creation) |
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7. [Usage](#usage) |
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8. [License](#license) |
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9. [Citation](#citation) |
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--- |
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### Dataset Description |
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**NumClaim** is a sentence‑level corpus for detecting **numerical claims** in financial text. |
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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} |
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--- |
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### Supported Tasks |
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| Task | Objective | Typical Metrics | |
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|------|-----------|-----------------| |
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| **Numerical Claim Classification** | Classify a sentence as `INCLAIM` or `OUTOFCLAIM`. | Accuracy, Precision, Recall, F1 | |
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| **Optimism Scoring** | Produce a continuous optimism score derived from claim likelihood (research use‑case in paper). | Spearman / Pearson correlation with returns | |
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--- |
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### Dataset Structure |
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```yaml |
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configs: |
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- config_name: main |
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data_files: |
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- split: train |
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path: main/train-* |
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- split: test |
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path: main/test-* |
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dataset_info: |
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features: |
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- context: string # Financial sentence |
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- response: string # Label: INCLAIM / OUTOFCLAIM |
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splits: |
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- name: train |
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num_examples: 2_144 |
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num_bytes: 317_988 |
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- name: test |
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num_examples: 537 |
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num_bytes: 79_011 |
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download_size: 208_114 |
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dataset_size: 396_999 |
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``` |
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### Data Fields |
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| Field | Type | Description | |
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|---------|--------|-------------------------------------------------------------------| |
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| context | string | Sentence from an analyst report or earnings call. | |
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| response| string | `INCLAIM` or `OUTOFCLAIM` label. | |
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--- |
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### Data Splits |
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| Split | # Sentences | Portion | |
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|-------|------------:|--------:| |
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| Train | 2 144 | 80 % | |
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| Test | 537 | 20 % | |
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| **Total** | **2 681** | **100 %** | |
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--- |
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### Dataset Creation |
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- **Source Collection** – Analyst reports (Thomson Reuters) and quarterly earnings‑call transcripts for U.S. public firms (2010 – 2023). |
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- **Sentence Filtering** – Retained only sentences containing a financial term, numeric value, and currency/percentage symbol. |
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- **Annotation** – Weak‑supervision rules augmented with subject‑matter‑expert knowledge produced initial labels; a subset was manually validated. |
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- **Quality Control** – Manual spot‑checks achieved > 0.9 label accuracy, and noisy sentences were removed. |
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--- |
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### Usage |
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```python |
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from datasets import load_dataset |
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numclaim = load_dataset("gtfintechlab/Numclaim") |
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sample = numclaim["train"][0] |
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print(sample["context"]) |
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print(sample["response"]) |
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``` |
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### License |
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Released under Creative Commons Attribution 4.0 International (CC BY 4.0). |
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### Citation |
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``` bibtex |
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@inproceedings{shah2024numclaim, |
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title = {Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis}, |
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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}, |
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booktitle = {Findings of the Association for Computational Linguistics}, |
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year = {2024}, |
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url = {https://arxiv.org/abs/2402.11728} |
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} |
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``` |
