Overview

• Graph-Neural-Networks (GNN) represents a given graph where the nodes are linked with edges into a vector space. Relational-Graph-Convolutional-Networks (R-GCN) introduced relation dependant graph representation. The typed dependency of given text forms triples of words and dependency (t_dep, dependency, t_gov) is similar to the triples of nodes and edge in a graph (d_i, rel, d_j) where t is token and d is data. Therefore, this project aims to implement pytorch dependency-graph-convolutional-networks in order to represent the dependency relations of each word from given text. The output of the dependency-graph-convolutional-networks is the token-level representation of the sum of the token and its dependency.

Brief description

• DepGCN.py

Output format

• output: List of tensor of the sum of token embedding itself and dependency relation that is connected to the governor. (list)

• text_processing.py

Output format

• input_tk_list (list): Tokens of given text based on the selected nlp pipeline.
• input_dep_list (list): Dependency triple of given text based on the selected nlp pipeline.

Prerequisites

• argparse
• torch
• stanza
• spacy

Parameters

• nlp_pipeline(str, defaults to "stanza"): NLP preprocessing pipeline.
• num_layers(int, defaults to 1): The number of hidden layers of the architecture.
• reverse(bool, defaults to True): Applying reverse dependency cases (gov -> dep) or not.

References

• Graph Convolutional Networks (GCN): Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
• Relational-Graph-Convolutional-Networks (R-GCN): Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. V. D., Titov, I., & Welling, M. (2018, June). Modeling relational data with graph convolutional networks. In European semantic web conference (pp. 593-607). Springer, Cham.
• Stanza: Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020). Stanza: A Python natural language processing toolkit for many human languages. arXiv preprint arXiv:2003.07082.
• Spacy: Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear (2017).