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Author Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados
Title Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents Type Conference Article
Year 2020 Publication 25th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages
Abstract The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach.
Address Virtual; January 2021
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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ CRV2020 Serial 3509
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