Imformation for NER datasets

constants.py

@@ -45,8 +45,33 @@ CITATIONS = {
           journal = {Genome biology},
           doi = {10.1186/gb-2008-9-s2-s2}
           }""",
-  "NCBI-disease-IOB":"",
-  "JNLPBA":"",
+  "NCBI-disease-IOB":"""@article{10.5555/2772763.2772800,
+          author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
+          title = {NCBI Disease Corpus},
+          year = {2014},
+          issue_date = {February 2014},
+          publisher = {Elsevier Science},
+          address = {San Diego, CA, USA},
+          volume = {47},
+          number = {C},
+          issn = {1532-0464},
+          abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
+          journal = {J. of Biomedical Informatics},
+          month = {feb},
+          pages = {1–10},
+          numpages = {10}}""",
+  "JNLPBA":"""@inproceedings{collier-kim-2004-introduction,
+          title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
+          author = "Collier, Nigel  and
+            Kim, Jin-Dong",
+          booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
+          month = aug # " 28th and 29th",
+          year = "2004",
+          address = "Geneva, Switzerland",
+          publisher = "COLING",
+          url = "https://aclanthology.org/W04-1213",
+          pages = "73--78",
+          }""",
 
 }
 
@@ -83,17 +108,42 @@ DESCRIPTIONS = {
                     and for each sentence a set of gene mentions 
                     (GENE annotations).
                     """,
-  "NCBI-disease-IOB":"",
-  "JNLPBA":"",
+  "NCBI-disease-IOB":"""The NCBI disease corpus is fully annotated at the mention 
+                      and concept level to serve as a research resource for the biomedical natural 
+                      language processing community.
+
+
+                    Corpus Characteristics
+                    ----------------------
+                    * 793 PubMed abstracts
+                    * 6,892 disease mentions
+                    * 790 unique disease concepts
+                    * Medical Subject Headings (MeSH®)
+                    * Online Mendelian Inheritance in Man (OMIM®)
+                    * 91% of the mentions map to a single disease concept
+                      **divided into training, developing and testing sets.
+                    Corpus Annotation
+                    * Fourteen annotators
+                    * Two-annotators per document (randomly paired)
+                    * Three annotation phases
+                    * Checked for corpus-wide consistency of annotations
+                    """,
+  "JNLPBA":"""The BioNLP / JNLPBA Shared Task 2004 involves the identification 
+                    and classification of technical terms referring to concepts of interest to 
+                    biologists in the domain of molecular biology. The task was organized by GENIA 
+                    Project based on the annotations of the GENIA Term corpus (version 3.02). 
+                    Corpus format: The JNLPBA corpus is distributed in IOB format, with each line 
+                    containing a single token and its tag, separated by a tab character. 
+                    Sentences are separated by blank lines.""",
 }
 
 HOMEPAGES = {
   "blurb": "https://microsoft.github.io/BLURB/index.html",
   "BC5CDR-chem-IOB":"https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus",
   "BC5CDR-disease-IOB":"https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus",
-  "BC2GM-IOB": "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/"
-  "NCBI-disease-IOB":"",
-  "JNLPBA":"",
+  "BC2GM-IOB": "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/",
+  "NCBI-disease-IOB":"https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/",
+  "JNLPBA":"http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004",
 
 }
 
@@ -103,6 +153,6 @@ DATA_URL = {
   "BC5CDR-chem-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
   "BC5CDR-disease-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
   "BC2GM-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
-  "NCBI-disease-IOB":"",
-  "JNLPBA":"",
+  "NCBI-disease-IOB": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
+  "JNLPBA": "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
 }