Hands-On Machine Learning for Algorithmic Trading

How to train your own word vector embeddings

Many tasks require embeddings or domain-specific vocabulary that pretrained models based on a generic corpus may not represent well or at all. Standard Word2vec models are not able to assign vectors to out-of-vocabulary words and instead use a default vector that reduces their predictive value.

For example, when working with industry-specific documents, the vocabulary or its usage may change over time as new technologies or products emerge. As a result, the embeddings need to evolve as well. In addition, corporate earnings releases use nuanced language not fully reflected in GloVe vectors pretrained on Wikipedia articles.

We will illustrate the Word2vec architecture using the Keras library that ...