Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain
This work addresses the need for automated role relevance prediction in financial domains with minimal domain expertise, though it is incremental as it builds on existing word embedding methods.
The paper tackles the problem of predicting role relevance between financial entities in text by using a word embedding-based architecture, achieving a minimal-expertise baseline that performs well across roles with simplicity and open-source tools.
Word embeddings have made enormous inroads in recent years in a wide variety of text mining applications. In this paper, we explore a word embedding-based architecture for predicting the relevance of a role between two financial entities within the context of natural language sentences. In this extended abstract, we propose a pooled approach that uses a collection of sentences to train word embeddings using the skip-gram word2vec architecture. We use the word embeddings to obtain context vectors that are assigned one or more labels based on manual annotations. We train a machine learning classifier using the labeled context vectors, and use the trained classifier to predict contextual role relevance on test data. Our approach serves as a good minimal-expertise baseline for the task as it is simple and intuitive, uses open-source modules, requires little feature crafting effort and performs well across roles.