In this paper, we report the results of our participation to the CLEF eHealth 2020 Task on “Multilingual Information Extraction”. This task focuses on coding of medical textual data using the International Statistical Classification of Diseases and Related Health Problems (ICD) in Spanish. The main objective of our participation to this task is the study of reproducible experiments that use minimal effort to be set up and run and that can be used as a baseline. The contribution of our experiments to this task can be summarized as follows: the implementation of a reproducible pipeline for text analysis that uses universal dependency parsing; an evaluation of simple classifiers based on perfect matches on different morphological levels together with a tf-idf approach.
As Simple as Possible: Using the R Tidyverse for Multilingual Information Extraction. IMS Unipd at CLEF eHealth 2020 Task 1
Di Nunzio G. M.
2020
Abstract
In this paper, we report the results of our participation to the CLEF eHealth 2020 Task on “Multilingual Information Extraction”. This task focuses on coding of medical textual data using the International Statistical Classification of Diseases and Related Health Problems (ICD) in Spanish. The main objective of our participation to this task is the study of reproducible experiments that use minimal effort to be set up and run and that can be used as a baseline. The contribution of our experiments to this task can be summarized as follows: the implementation of a reproducible pipeline for text analysis that uses universal dependency parsing; an evaluation of simple classifiers based on perfect matches on different morphological levels together with a tf-idf approach.Pubblicazioni consigliate
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