TY - GEN
T1 - Enriching Graph Representations of Text
T2 - 9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020
AU - Mandalios, Alexios
AU - Chortaras, Alexandros
AU - Stamou, Giorgos
AU - Vazirgiannis, Michalis
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Graph based representations have been utilized to achieve state-of-the-art performance in text classification tasks. The same basic structure underlies knowledge graphs, large knowledge bases that contain rich information about the world. This paper capitalises on the graph of words model and enriches it with concepts from knowledge graphs, resulting in more powerful hybrid representations of a corpus. We focus on the domain of medical text classification and medical ontologies in order to test our proposed methods and analyze different alternatives in terms of text representation models and knowledge injection techniques. The method we present produces text representations that are both explainable and effective in improving the accuracy on the OHSUMED classification task, surpassing neural network architectures such as GraphStar and Text GCN.
AB - Graph based representations have been utilized to achieve state-of-the-art performance in text classification tasks. The same basic structure underlies knowledge graphs, large knowledge bases that contain rich information about the world. This paper capitalises on the graph of words model and enriches it with concepts from knowledge graphs, resulting in more powerful hybrid representations of a corpus. We focus on the domain of medical text classification and medical ontologies in order to test our proposed methods and analyze different alternatives in terms of text representation models and knowledge injection techniques. The method we present produces text representations that are both explainable and effective in improving the accuracy on the OHSUMED classification task, surpassing neural network architectures such as GraphStar and Text GCN.
KW - Graph of words
KW - Knowledge graphs
KW - Medical information systems
KW - Natural language processing
KW - Semantic enrichment of text
UR - https://www.scopus.com/pages/publications/85101823991
U2 - 10.1007/978-3-030-65351-4_8
DO - 10.1007/978-3-030-65351-4_8
M3 - Conference contribution
AN - SCOPUS:85101823991
SN - 9783030653507
T3 - Studies in Computational Intelligence
SP - 92
EP - 103
BT - Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
A2 - Benito, Rosa M.
A2 - Cherifi, Chantal
A2 - Cherifi, Hocine
A2 - Moro, Esteban
A2 - Rocha, Luis Mateus
A2 - Sales-Pardo, Marta
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 December 2020 through 3 December 2020
ER -