--- 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. # Tasks The main task that this data was designed for is factuality classification.