README.md

---
pretty_name: PubMed Cognitive Control Abstracts
license:
- cc-by-4.0
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- topic-classification
- semantic-similarity-classification
size_categories:
- 100K<n<1M
paperswithcode_id: linking-theories-and-methods-in-cognitive
inference: false
model-index: 
  - name: cogtext-pubmed
    results: []
source_datasets:
- original
language_creators:
- found
- expert-generated
configs:
- pubmed
- pubmed20pct
- lexicon
- pubmed_gp3ada
---

## Dataset Description
- **Homepage:** [ArXiv preprint](https://arxiv.org/abs/1011.6217)
- **Repository:** [Linking Theories and Methods of Cognitive Control](https://github.com/morteza/cogtext)
- **Point of Contact:** [Morteza Ansarinia]([email protected])

We performed automated text analyses on a large body of scientific texts (385705 scientific abstracts) and created a joint representation of cognitive control tasks and constructs.

Abstracts were first mapped into an embedding space using GPT-3 and Top2Vec models. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph.

**CogText** dataset contains a collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings. See [CogText on GitHub](https://github.com/morteza/cogtext) for the details and codes.

## Citation

To cite the paper use the following entry:

```
@misc{cogtext2022,
  author = {Morteza Ansarinia and
            Paul Schrater and
            Pedro Cardoso-Leite},
  title = {Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control},
  year = {2022},
  url = {https://arxiv.org/abs/2203.11016}
}
```