====== List of existing resources useful for natural language processing in the pharmacovigilance domain ======
  * **source**: type of source, e.g., scientific paper, abstract, drug leaflet, patient forum, tweet, etc.
  * **lang** = languages: comma-separated list of 2-letter ISO codes
  * **description**: short characterization of the corpus
  * **noteworthiness**: any specific feature of this dataset
  * **NER**: are entities annotated, and for what types of entities
  * **linking**: is entity linking provided, and to what ontologies
  * **REL**: are relations annotated
    * IE = information extraction style: between entity instances (one per pair of entity spans),
    * KB = knowledge-base style: between entities (one per text and pair of [linked] entities),
    * CL = text classification style: presence of a relation between entity types (one per text and pair of entity types); if only one type of relation is considered, this is a binary text classification task
  * **REL list**: if REL is non null, list of annotated relations
  * **format**: CONLL, BRAT, etc.
  * **size**: number of language units such as documents, sentences, words (please no megabytes)
  * **publication**: reference to a publication (peer-reviewed rather than preprint)
  * **URL**: URL where the dataset can be downloaded or is described


^ name	^ source	^ lang	^ description	^ noteworthiness	^ NER	^ linking	^ REL	^ REL list	^ format	^ size	^ publication	^ URL	^
^ TLC	| patient forum	| de	| dataset annotated with layman expressions: Fachterm, Laienbegriff, Abkürzung	| 	| layman terms, including their associated technical terms; technical term with a rather layman term	| no	| no	| 	| 	| BRAT	| 4000 documents	| https://www.aclweb.org/anthology/2020.lrec-1.759/	| http://macss.dfki.de/data/LREC2020/TLC_v01.tar.gz	|