This inventory contains a set of terms that are relevant to the study of medical history. The inventory is organised as a set of "heading terms", belonging to one of seven different semantic categories, each of which is accompanied by a set of semantically-related terms. There are around 175,0...
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. The tool is provided as a UIMA component, which forms part of the in-built library of...
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 corpus consists of 1000 MEDLINE abstracts. It is a subset of the original GENIA POS & term corpus, which was selected using the three MeSH terms human, blood cells and transcription factors. In each sentence, three types of information are annotated 1) biomedical terms are identified and assi...
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.
A corpus of manually annotated event hierarchies in news stories.
A computational lexicon for Portuguese that provides mappings between verbs and their nominalizations.
The U-Compare Workbench is a graphical user interface that operates on top of the U-Compare platform. The U-Compare platform allows users to build and evaluate NLP workflows. Workflows consist of one or more components, consisting of corpus readers and tools, such as tokenisers, POS taggers, name...
Treat is a toolkit for natural language processing and computational linguistics in Ruby. The Treat project aims to build a language- and algorithm- agnostic NLP framework for Ruby with support for tasks such as document retrieval, text chunking, segmentation and tokenization, natural language pa...
Yake! (Campos et al. 2020) is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike most of the systems, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, we follow a...