Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach
This work addresses annotation scarcity and class imbalance for event extraction in crude oil news, supporting downstream tasks like price prediction and business decision-making, but it is incremental as it applies existing transfer learning methods to a specific domain.
The paper tackles the challenge of event extraction in the finance domain with limited annotated data by developing a transfer learning framework for crude oil-related events, improving F1 and MCC1-scores over baseline models. It also classifies event properties like Polarity, Modality, and Intensity to assess factual certainty.
One of the challenges in event extraction via traditional supervised learning paradigm is the need for a sizeable annotated dataset to achieve satisfactory model performance. It is even more challenging when it comes to event extraction in the finance and economics domain, a domain with considerably fewer resources. This paper presents a complete framework for extracting and processing crude oil-related events found in CrudeOilNews corpus, addressing the issue of annotation scarcity and class imbalance by leveraging on the effectiveness of transfer learning. Apart from event extraction, we place special emphasis on event properties (Polarity, Modality, and Intensity) classification to determine the factual certainty of each event. We build baseline models first by supervised learning and then exploit Transfer Learning methods to boost event extraction model performance despite the limited amount of annotated data and severe class imbalance. This is done via methods within the transfer learning framework such as Domain Adaptive Pre-training, Multi-task Learning and Sequential Transfer Learning. Based on experiment results, we are able to improve all event extraction sub-task models both in F1 and MCC1-score as compared to baseline models trained via the standard supervised learning. Accurate and holistic event extraction from crude oil news is very useful for downstream tasks such as understanding event chains and learning event-event relations, which can be used for other downstream tasks such as commodity price prediction, summarisation, etc. to support a wide range of business decision making.