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

README.md

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+
+---
+language: 
+- en
+license: unknown
+license_bigbio_shortname: UNKNOWN
+pretty_name: CHEMDNER
+---
+
+
+# Dataset Card for CHEMDNER
+
+## Dataset Description
+
+- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** Named Entity Recognition, Text Classification
+
+
+We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that
+contain a total of 84,355 chemical entity mentions labeled manually by expert
+chemistry literature curators, following annotation guidelines specifically
+defined for this task. The abstracts of the CHEMDNER corpus were selected to be
+representative for all major chemical disciplines. Each of the chemical entity
+mentions was manually labeled according to its structure-associated chemical
+entity mention (SACEM) class: abbreviation, family, formula, identifier,
+multiple, systematic and trivial.
+
+
+
+## Citation Information
+
+```
+@article{Krallinger2015,
+  title        = {The CHEMDNER corpus of chemicals and drugs and its annotation principles},
+  author       = {
+    Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez,
+    Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan
+    and Ji, Donghong and Lowe, Daniel M. and Sayle, Roger A. and
+    Batista-Navarro, Riza Theresa and Rak, Rafal and Huber, Torsten and
+    Rockt{"a}schel, Tim and Matos, S{'e}rgio and Campos, David and Tang,
+    Buzhou and Xu, Hua and Munkhdalai, Tsendsuren and Ryu, Keun Ho and Ramanan,
+    S. V. and Nathan, Senthil and {{Z}}itnik, Slavko and Bajec, Marko and
+    Weber, Lutz and Irmer, Matthias and Akhondi, Saber A. and Kors, Jan A. and
+    Xu, Shuo and An, Xin and Sikdar, Utpal Kumar and Ekbal, Asif and Yoshioka,
+    Masaharu and Dieb, Thaer M. and Choi, Miji and Verspoor, Karin and Khabsa,
+    Madian and Giles, C. Lee and Liu, Hongfang and Ravikumar, Komandur
+    Elayavilli and Lamurias, Andre and Couto, Francisco M. and Dai, Hong-Jie
+    and Tsai, Richard Tzong-Han and Ata, Caglar and Can, Tolga and Usi{'e},
+    Anabel and Alves, Rui and Segura-Bedmar, Isabel and Mart{'i}nez, Paloma
+    and Oyarzabal, Julen and Valencia, Alfonso
+  },
+  year         = 2015,
+  month        = {Jan},
+  day          = 19,
+  journal      = {Journal of Cheminformatics},
+  volume       = 7,
+  number       = 1,
+  pages        = {S2},
+  doi          = {10.1186/1758-2946-7-S1-S2},
+  issn         = {1758-2946},
+  url          = {https://doi.org/10.1186/1758-2946-7-S1-S2},
+  abstract     = {
+    The automatic extraction of chemical information from text requires the
+    recognition of chemical entity mentions as one of its key steps. When
+    developing supervised named entity recognition (NER) systems, the
+    availability of a large, manually annotated text corpus is desirable.
+    Furthermore, large corpora permit the robust evaluation and comparison of
+    different approaches that detect chemicals in documents. We present the
+    CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
+    total of 84,355 chemical entity mentions labeled manually by expert
+    chemistry literature curators, following annotation guidelines specifically
+    defined for this task. The abstracts of the CHEMDNER corpus were selected
+    to be representative for all major chemical disciplines. Each of the
+    chemical entity mentions was manually labeled according to its
+    structure-associated chemical entity mention (SACEM) class: abbreviation,
+    family, formula, identifier, multiple, systematic and trivial. The
+    difficulty and consistency of tagging chemicals in text was measured using
+    an agreement study between annotators, obtaining a percentage agreement of
+    91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts)
+    we provide not only the Gold Standard manual annotations, but also mentions
+    automatically detected by the 26 teams that participated in the BioCreative
+    IV CHEMDNER chemical mention recognition task. In addition, we release the
+    CHEMDNER silver standard corpus of automatically extracted mentions from
+    17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus
+    in the BioC format has been generated as well. We propose a standard for
+    required minimum information about entity annotations for the construction
+    of domain specific corpora on chemical and drug entities. The CHEMDNER
+    corpus and annotation guidelines are available at:
+    ttp://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/
+  }
+}
+
+```