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--- |
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dataset_info: |
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features: |
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- name: factuality_value |
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dtype: string |
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- name: predicat@xml:space |
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dtype: string |
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- name: predicat@charOffset |
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dtype: string |
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- name: predicat@headOffset |
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dtype: string |
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- name: predicat@id |
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dtype: string |
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- name: predicat@text |
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dtype: string |
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- name: predicat@type |
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dtype: string |
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- name: predicat@charOffsetMin |
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dtype: int64 |
|
- name: predicat@charOffsetMax |
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dtype: int64 |
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- name: subject@xml:space |
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dtype: string |
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- name: subject@charOffset |
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dtype: string |
|
- name: subject@headOffset |
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dtype: string |
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- name: subject@id |
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dtype: string |
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- name: subject@text |
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dtype: string |
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- name: subject@type |
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dtype: string |
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- name: subject@charOffsetMin |
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dtype: int64 |
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- name: subject@charOffsetMax |
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dtype: int64 |
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- name: object@xml:space |
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dtype: string |
|
- name: object@charOffset |
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dtype: string |
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- name: object@headOffset |
|
dtype: string |
|
- name: object@id |
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dtype: string |
|
- name: object@text |
|
dtype: string |
|
- name: object@type |
|
dtype: string |
|
- name: object@charOffsetMin |
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dtype: int64 |
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- name: object@charOffsetMax |
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dtype: int64 |
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- name: id |
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dtype: string |
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- name: raw_sent_text |
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dtype: string |
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- name: sent_charOffset |
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dtype: string |
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- name: sent_charOffsetMin |
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dtype: int64 |
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- name: sent_charOffsetMax |
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dtype: int64 |
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- name: formated_sentence |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2278527 |
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num_examples: 3149 |
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- name: test |
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num_bytes: 1559577 |
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num_examples: 2179 |
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download_size: 1308178 |
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dataset_size: 3838104 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- text-classification |
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--- |
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# Introduction |
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Factuality classification/quantification is one of the most difficult tasks in NLP. |
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As apposed to sentiment analysis or other NLP tasks with statistical patterns, this task requires syntactic dependency patterns (aka, paradigmatics). |
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In fact, [N. Jiang et al](https://aclanthology.org/2021.tacl-1.64/) have demonstrated BERTs inability to recognize paradigmatics. |
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# Dataset Description |
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This dataset was constructed by [H. Kilicoglu et al](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179926) to predict the factuality expressed in text about a certain event/triple. |
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Each triple is composed out of a subject-predicate-object. The dataset contains the position of each triple in a sentence, the raw sentence and a masked sentence where those positions are marked with special characters. |
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It also contains the factuality value assigned by the [H. Kilicoglu et al](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179926). |
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The sentences are taken from the PubMed biomedical abstracts. |
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The dataset factuality classes belong to a factuality scale introduced by [H. Kilicoglu et al](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179926). |
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The following figure shows this factuality scale. Counterfact, Doubtful, Possible, Probable, Certain represent varyibg levels of certainty, while Uncommited and Conditional represent a lack of information that would express factuality regarding a claim or an event. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/656f0e5abaa95d8b8cc90a37/Xc8UIt0ZlmD2-Eho9MCYs.png" width="500"/> |
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# Tasks |
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The main task that this data was designed for is factuality classification. |
