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Word embeddings have made enormous inroads in recent years in a wide varietyof text mining applications. In this paper, we explore a word embedding-basedarchitecture for predicting the relevance of a role between two financialentities within the context of natural language sentences. In this extendedabstract, we propose a pooled approach that uses a collection of sentences totrain word embeddings using the skip-gram word2vec architecture. We use theword embeddings to obtain context vectors that are assigned one or more labelsbased on manual annotations. We train a machine learning classifier using thelabeled context vectors, and use the trained classifier to predict contextualrole relevance on test data. Our approach serves as a good minimal-expertisebaseline for the task as it is simple and intuitive, uses open-source modules,requires little feature crafting effort and performs well across roles.

Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain