The GENIA tagger analyzes English sentences and outputs the base forms, part-of-speech tags, chunk tags, and named entity tags. The tagger is specifically tuned for biomedical text such as MEDLINE abstracts.
PhenoCHF is an annotated corpus consisting of documents belonging to two different text types (i.e., narrative reports from electronic health records (EHRs) and literature articles). It is manually annotated by medical doctors with detailed information relating to mentions of phenotype concepts a...
The HIMERA annotated corpus contains a set of published historical medical documents that have been manually annotated with semantic information that is relevant to the study of medical history and public health. Specifically, annotations correspond to seven different entity types and two differe...
The Complex Word (CW) Corpus contains 731 sentences each with one annotated CW. These simplifications were mined from Simple Wikipedia edit histories. Each entry gives an example of a sentence requiring simplification by means of a single lexical edit. This resource is primarily designed for t...
Web service created by exporting UIMA-based workflow from the U-Compare text mining system. Functionality: Identifies biomedical named entities (genes and proteins) in plain text. Also identifies sentences. Tools in workflow: Cafetiere Sentence Splitter (University of Manchester), NEMine (Univ...
GREC is a semantically annotated corpus of 240 MEDLINE abstracts (167 on the subject of E. coli species and 73 on the subject of the Human species) which is intended for training IE systems and/or resources which are used to extract events from biomedical literature.
This resource includes the distributional semantic vectors used for the replication of the TakeLab system (https://github.com/nlx-group/arct-rep-rev). The TakeLab system is an automatic classifier for the Argument Reasoning Comprehension Task (https://www.aclweb.org/anthology/S18-1121/). The ...
Adimen-SUMO is an off-the-shelf first-order ontology that has been obtained by reengineering out of the 88% of SUMO (Suggested Upper Merged Ontology). Adimen-SUMO can be used appropriately by FO theorem provers (like E-Prover or Vampire) for formal reasoning.
A corpus of 2,000 MEDLINE abstracts, collected using the three MeSH terms human, blood cells and transcription factors. The corpus is available in three formats: 1) A text file containing part-of-speech (POS) annotation, based on the Penn Treebank format, 2) An XML file containing inline POS anno...
Tweet corpus