Authors:
Mohamed Landolsi
;
Lobna Hlaoua
and
Lotfi Ben Romdhane
Affiliation:
MARS Research Lab LR17ES05, SDM Research Group, ISITCom, University of Sousse, Hammam Sousse, Tunisia
Keyword(s):
Natural Language Processing, Medical Information Extraction, Relation Extraction, Clinical Named Entities, Graph Convolutional Network.
Abstract:
A large number of electronic medical documents are generated by specialists, containing valuable information for various medical tasks such as medical prescriptions. Extracting this information from extensive natural language text can be challenging. Named Entity Recognition (NER) and Relation Extraction (RE) are key tasks in clinical information extraction. Systems often rely on machine learning and rule-based techniques. Modern methods involve dependency parsing and graph-based deep learning algorithms. However, the effectiveness of these techniques and certain features is not thoroughly studied. Additionally, it would be advantageous to properly integrate rules with deep learning models. In this paper, we introduce GREED (Graph learning based Relation Extraction with Entity and Dependency relations). GREED is based on graph classification using Graph Convolutional Network (GCN). We transform each sentence into a weighted graph via dependency parsing. Words are represented with fea
tures that capture co-occurrence, dependency type, entities, and relation verbs, with focus on the entity pair. Experiments on clinical records (i2b2/VA 2010) show that relevant features efficiently integrated with GCN achieve higher performance.
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