Deriving an English Biomedical Silver Standard Corpus for CLEF-ER

We present a supervised Clinical named Entity Recognition system that can detect the named entities from a French medical data, using an extensive list of features, with an F1 Score

The first method involves a novel zero-resource approach based on unsuper- vised acoustic only modeling of speech involving word discovery, and the second

A pre- annotation consists of the span of text corresponding with the concept, and its preferred name, semantic type, semantic group, and CUI (all based on the

The corpora have been selected from different resources and for the purpose to serve two objectives: (1) enabling extraction of novel terms for the multi- lingual TR as part of

The task of this challenge was to identify hitherto unknown translation equivalents for biomedical terms from several parallel text corpora.. The languages being covered are

This work reports baseline results for the CLEF 2008 Medical Automatic Annotation Task (MAAT) by applying a classifier with a fixed parameter set to all tasks 2005 – 2008..

To obtain estimates for t d and t n , the development set is used: inspecting the classifiction for this set, the results are sorted based on the best distances from neighbors from

When the question asks about a Named Entity such as MEASURE, PERSON, LOCATION, ORGANIZATION, DATE, the UAIC answer extractor looks for all the expressions tagged with the desired