Hierarchical Contextual Document Embeddings for Long Financial Text Regression

Code and data for "Hierarchical Contextual Document Embeddings for Long Financial Text Regression"

Prerequisites

This code is written in python. To use it you will need:

• Python 3.6
• PyTorch [PyTorch] = 1.2.0
• A recent version of scikit-learn
• A recent version of Numpy
• A recent version of NLTK
• Tensorflow = 1.15.2

Getting started

We provide all the document embeddings [sum, max, mean, mean_max, max_mean, concat] in data directory.

We also provide the script create_emb.py to extract the above embeddings.

Additionally, we used extract_all_layers.py on our text documents to extract all 12 layers' hidden weights.

data_2436.csv contains tokenized text for different features such as words, sentiment_words and syntactically_expanded_sentiment_words. This data file can be found at Zenodo