Datasets:
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---
dataset_info:
features:
- name: factuality_value
dtype: string
- name: predicat@xml:space
dtype: string
- name: predicat@charOffset
dtype: string
- name: predicat@headOffset
dtype: string
- name: predicat@id
dtype: string
- name: predicat@text
dtype: string
- name: predicat@type
dtype: string
- name: predicat@charOffsetMin
dtype: int64
- name: predicat@charOffsetMax
dtype: int64
- name: subject@xml:space
dtype: string
- name: subject@charOffset
dtype: string
- name: subject@headOffset
dtype: string
- name: subject@id
dtype: string
- name: subject@text
dtype: string
- name: subject@type
dtype: string
- name: subject@charOffsetMin
dtype: int64
- name: subject@charOffsetMax
dtype: int64
- name: object@xml:space
dtype: string
- name: object@charOffset
dtype: string
- name: object@headOffset
dtype: string
- name: object@id
dtype: string
- name: object@text
dtype: string
- name: object@type
dtype: string
- name: object@charOffsetMin
dtype: int64
- name: object@charOffsetMax
dtype: int64
- name: id
dtype: string
- name: raw_sent_text
dtype: string
- name: sent_charOffset
dtype: string
- name: sent_charOffsetMin
dtype: int64
- name: sent_charOffsetMax
dtype: int64
- name: formated_sentence
dtype: string
splits:
- name: train
num_bytes: 2278527
num_examples: 3149
- name: test
num_bytes: 1559577
num_examples: 2179
download_size: 1308178
dataset_size: 3838104
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-classification
---
# Introduction
Factuality classification/quantification is one of the most difficult tasks in NLP.
As apposed to sentiment analysis or other NLP tasks with statistical patterns, this task requires syntactic dependency patterns (aka, paradigmatics).
In fact, [N. Jiang et al](https://aclanthology.org/2021.tacl-1.64/) have demonstrated BERTs inability to recognize paradigmatics.
# Dataset Description
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.
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.
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).
The sentences are taken from the PubMed biomedical abstracts.
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).
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.
<img src="https://cdn-uploads.huggingface.co/production/uploads/656f0e5abaa95d8b8cc90a37/Xc8UIt0ZlmD2-Eho9MCYs.png" width="500"/>
# Tasks
The main task that this data was designed for is factuality classification. |