  Graph neural networks (GNNs) have been utilized for various natural language
processing (NLP) tasks lately. The ability to encode corpus-wide features in
graph representation made GNN models popular in various tasks such as document
classification. One major shortcoming of such models is that they mainly work
on homogeneous graphs, while representing text datasets as graphs requires
several node types which leads to a heterogeneous schema. In this paper, we
propose a transductive hybrid approach composed of an unsupervised node
representation learning model followed by a node classification/edge prediction
model. The proposed model is capable of processing heterogeneous graphs to
produce unified node embeddings which are then utilized for node classification
or link prediction as the downstream task. The proposed model is developed to
classify stock market technical analysis reports, which to our knowledge is the
first work in this domain. Experiments, which are carried away using a
constructed dataset, demonstrate the ability of the model in embedding
extraction and the downstream tasks.
