# Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain

## TL;DR

This paper presents a simple, open-source, word embedding-based method for predicting role relevance between financial entities in sentences, requiring minimal domain expertise and demonstrating strong performance.

## Contribution

It introduces a minimal-expertise, pooled word embedding approach using skip-gram word2vec and machine learning classifiers for role relevance prediction in finance.

## Key findings

- Effective role relevance prediction across multiple roles
- Requires minimal feature engineering and domain knowledge
- Uses open-source tools with good performance

## Abstract

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.