A Comprehensive Overview of Graph Neural Networks for Natural Language Processing

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Machine Learning

Computation and Language

A Comprehensive Overview of Graph Neural Networks for Natural Language Processing

Graph Neural Networks for Natural Language Processing: A Survey

In this survey, we present a comprehensive overview ongraphneural networks(gnns)for natural language processing (nlp).

We propose a new taxonomy of gnns for nlp, whichsystematically organizes existing research of gnns for nlp along three axes : graph construction,graph representation learning, and graph based encoder-decoder models.

We further introducea large number of nlp applications that are exploiting the power of gnns and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes.

Finally, we discussvarious outstanding challenges for making the full use of gnns for nlp as well as future researchdirections.

Authors

Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long

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