TMUNLPG1 at the NTCIR-18 FinArg-2 Task

The TMUNLPG1 team participated in the FinArg-2 Task of NTCIR-18, focusing on the Detection of Argument Temporal References and Assessment of the Claim's Validity Period in the finance domain using Earning Conference Call and Social Media datasets. The team ranked 6th and 2nd in these subtasks, respectively. This paper presents the team's methodologies, results, and conclusions. For Earnings Conference Call (ECC) Argument Temporal References, we utilized a combination of feature engineering, ensemble strategy, and data augmentation to achieve a Micro F1 score of 0.6905. In Social Media Assessment of the Claim's Validity Period, we developed an enhanced approach combining domain-specific transformer architectures with statistical feature engineering. By integrating FinBERT with Log-Likelihood Ratio (LLR) and Pointwise Mutual Information (PMI) features, we achieved a Micro F1 score of 0.742 on the unified dataset and demonstrated robust performance on the test set. The methodology incorporates weighted pooling strategies and adaptive learning rate optimization to improve temporal validity prediction accuracy. Our results highlight the effectiveness of combining domain-specific language models with traditional statistical approaches in financial text analysis, contributing to advancements in temporal natural language processing for the financial domain.