Comparison of text preprocessing methods
CP Chai - Natural Language Engineering, 2023 - cambridge.org
Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …
a key area that directly affects the natural language processing (NLP) application results. For …
[HTML][HTML] Clinical information extraction at the CLEF eHealth evaluation lab 2016
This paper reports on Task 2 of the 2016 CLEF eHealth evaluation lab which extended the
previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task …
previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task …
SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes
Background Despite a wide adoption of English in science, a significant amount of
biomedical data are produced in other languages, such as French. Yet a majority of natural …
biomedical data are produced in other languages, such as French. Yet a majority of natural …
[PDF][PDF] KFU at CLEF eHealth 2017 Task 1: ICD-10 Coding of English Death Certificates with Recurrent Neural Networks.
This paper describes the participation of the KFU team in the CLEF eHealth 2017 challenge.
Specifically, we participated in Task 1, namely “Multilingual Information Extraction-ICD-10 …
Specifically, we participated in Task 1, namely “Multilingual Information Extraction-ICD-10 …
[HTML][HTML] Building a semantic health data warehouse in the context of clinical trials: development and usability study
Background: The huge amount of clinical, administrative, and demographic data recorded
and maintained by hospitals can be consistently aggregated into health data warehouses …
and maintained by hospitals can be consistently aggregated into health data warehouses …
[PDF][PDF] Fusion Methods for ICD10 Code Classification of Death Certificates in Multilingual Corpora.
M Ebersbach, R Herms, M Eibl - CLEF (Working Notes), 2017 - ceur-ws.org
In this working notes paper, we present our methodology and the results for Task 1 of the
CLEF eHealth Evaluation Lab 2017. This benchmark addresses information extraction in …
CLEF eHealth Evaluation Lab 2017. This benchmark addresses information extraction in …
Cross-lingual candidate search for biomedical concept normalization
Biomedical concept normalization links concept mentions in texts to a semantically
equivalent concept in a biomedical knowledge base. This task is challenging as concepts …
equivalent concept in a biomedical knowledge base. This task is challenging as concepts …
Cimind: A phonetic-based tool for multilingual named entity recognition in biomedical texts
Background Extracting concepts from biomedical texts is a key to support many advanced
applications such as biomedical information retrieval. However, in clinical notes Named …
applications such as biomedical information retrieval. However, in clinical notes Named …
[PDF][PDF] WBI at CLEF eHealth 2018 Task 1: Language-independent ICD-10 Coding using Multi-lingual Embeddings and Recurrent Neural Networks.
This paper describes the participation of the WBI team in the CLEF eHealth 2018 shared
task 1 (“Multilingual Information Extraction-ICD-10 coding”). Our contribution focus on the …
task 1 (“Multilingual Information Extraction-ICD-10 coding”). Our contribution focus on the …
[PDF][PDF] SIBM at CLEF eHealth Evaluation Lab 2017: Multilingual Information Extraction with CIM-IND.
This paper presents SIBM's participation in the Task 1: Multilingual Information Extraction-
ICD10 coding of the CLEF eHealth 2017 evaluation initiative which focuses on named entity …
ICD10 coding of the CLEF eHealth 2017 evaluation initiative which focuses on named entity …