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{{See also|Natural language processing}}
Graph-based representation of text helps to capture deeper semantic relationships between words. Many studies have used graph networks to enhance performance in various text processing tasks such as text classification, question answering, Neural Machine Translation (NMT), event extraction, fact verification, etc.<ref>{{Cite journal |last1=Zhou |first1=Jie |last2=Cui |first2=Ganqu |last3=Hu |first3=Shengding |last4=Zhang |first4=Zhengyan |last5=Yang |first5=Cheng |last6=Liu |first6=Zhiyuan |last7=Wang |first7=Lifeng |last8=Li |first8=Changcheng |last9=Sun |first9=Maosong |date=2020-01-01 |title=Graph neural networks: A review of methods and applications |url=https://www.sciencedirect.com/science/article/pii/S2666651021000012 |journal=AI Open |volume=1 |pages=57–81 |doi=10.1016/j.aiopen.2021.01.001 |issn=2666-6510|doi-access=free }}</ref>
==References==
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