DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features
This work addresses a domain-specific problem for financial NLP, but it is incremental as it builds on existing shared task methods.
The paper tackled financial hypernym detection by augmenting terms with definitions and using a Logistic Regression classifier with word embeddings and features, achieving a 4th-place ranking on the FinSim-3 leaderboard.
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.