README.md

---
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- entity-linking-classification
- coreference-resolution
license: apache-2.0
language:
- en
tags:
- Named Entities Recognition
- Scientific Literature
pretty_name: A Large-Scale Corpus for Detecting Scientific Mentions
size_categories:
- 1M<n<10M
annotations_creators:
- expert-generated
viewer: true
configs:
- config_name: default
  data_files:
  - split: test
    path: "SciDMT_E_sentences.json"
---

# Description
We present SciDMT, an enhanced and expanded corpus for scientific mention detection, offering a significant advancement over existing related resources. SciDMT contains annotated scientific documents for datasets (D), methods (M), and tasks (T). 
The corpus consists of two components:
1) the SciDMT main corpus, which includes 48 thousand scientific articles with over 1.8 million weakly annotated mention annotations in the format of in-text span, and
2) an evaluation set, which comprises 100 scientific articles manually annotated for evaluation purposes.
To the best of our knowledge, SciDMT is the largest corpus for scientific entity mention detection. The corpus’s scale and diversity are instrumental in developing and refining models for tasks such as indexing scientific papers, enhancing information retrieval, and improving the accessibility of scientific knowledge. We demonstrate the corpus’s utility through experiments with advanced deep learning architectures like SciBERT and GPT-3.5. Our findings establish performance baselines and highlight unresolved challenges in scientific mention detection. SciDMT serves as a robust benchmark for the research community, encouraging the development of innovative models to further the field of scientific information extraction.

# Files

    'DICT': './SciDMT_dict.json',

    # machine learning inputs at sentence level
    'sent_xy': './SciDMT_sentences.p', 
    'sent_eval': './SciDMT_E_sentences.json',
    'sent_split': './SciDMT_sentences_split.json',

    # document level inputs
    'doc_split': './SciDMT_split.json',
    'doc_eval': './SciDMT_E_human_annotations.json',
    'doc_text_and_meta': './SciDMT_papers.csv',

# Usage
SciDMT_demo.ipynb describes how to open the files and print out the file structures. 

# Citation
The paper is recently accepted by a conference. The related citation info will be posted soon.