Enhanced Conditional Random Field Models for Cause and Effect Detection in Financial Documents

This paper addresses the Financial Document Causality Detection Task (FinCausal-2023), aiming to uncover the intricate causal relationships embedded in financial texts. The methodology proposed incorporates diverse natural language processing techniques, including Word2Vec embeddings, BERT embeddings, contextual encoding with BERT, token classification using SVM, and Conditional Random Fields (CRF). The study proposes a novel method to enhance the performance of CRFs using features created from a contextual based classification model and compares the same with the SOTA methods. Evaluation metrics, including precision, recall, F1-score, and an exact match percentage, assess the effectiveness of the proposed methodologies.The literature review section provides insights into previous work in financial causality detection, covering shared tasks, and models such as sequence labeling, etc. The paper concludes with results presented and a discussion of their implications, contributing to the ongoing discourse on causality detection in financial narratives.