upload hub_repos/cantemist/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- es
+license: cc-by-4.0
+license_bigbio_shortname: CC_BY_4p0
+pretty_name: CANTEMIST
+---
+
+
+# Dataset Card for CANTEMIST
+
+## Dataset Description
+
+- **Homepage:** https://temu.bsc.es/cantemist/?p=4338
+- **Pubmed:** False
+- **Public:** True
+- **Tasks:** Named Entity Recognition, Named Entity Disambiguation, Text Classification
+
+
+Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
+
+The original dataset is distributed in Brat format, and was randomly sampled into 3 subsets. The training, development and test sets contain 501, 500 and 300 documents each, respectively.
+
+This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL. The task is divided in 3 subtasks: CANTEMIST-NER, CANTEMIST_NORM and CANTEMIST-CODING.
+
+CANTEMIST-NER track: requires finding automatically tumor morphology mentions. All tumor morphology mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. 
+
+CANTEMIST-NORM track: clinical concept normalization or named entity normalization task that requires to return all tumor morphology entity mentions together with their corresponding eCIE-O-3.1 codes i.e. finding and normalizing tumor morphology mentions.
+
+CANTEMIST-CODING track: requires returning for each of document a ranked list of its corresponding ICD-O-3 codes. This it is essentially a sort of indexing or multi-label classification task or oncology clinical coding. 
+
+For further information, please visit https://temu.bsc.es/cantemist or send an email to [email protected]
+
+
+
+## Citation Information
+
+```
+@article{miranda2020named,
+  title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
+  author={Miranda-Escalada, Antonio and Farr{'e}, Eul{\`a}lia and Krallinger, Martin},
+  journal={IberLEF@ SEPLN},
+  pages={303--323},
+  year={2020}
+}
+
+```