DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features

TL;DR

This paper describes a system for classifying financial terms into hypernyms using augmented definitions, word embeddings, and distance-based features, achieving competitive results in a shared task.

Contribution

The paper introduces a novel approach combining augmented definitions, multiple word representations, and out-of-vocabulary handling for financial hypernym detection.

Findings

Achieved 4th place in FinSim-3 leaderboard.

Improved performance with out-of-vocabulary replacement methods.

Demonstrated effectiveness of multiple financial corpora embeddings.

Abstract

We present the submission of team DICoE for FinSim-3, the 3rd Shared Task on Learning Semantic Similarities for the Financial Domain. The task provides a set of terms in the financial domain and requires to classify them into the most relevant hypernym from a financial ontology. After augmenting the terms with their Investopedia definitions, our system employs a Logistic Regression classifier over financial word embeddings and a mix of hand-crafted and distance-based features. Also, for the first time in this task, we employ different replacement methods for out-of-vocabulary terms, leading to improved performance. Finally, we have also experimented with word representations generated from various financial corpora. Our best-performing submission ranked 4th on the task's leaderboard.