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Feature Fusion with Hand-crafted and Transfer Learning Embeddings for Cause-Effect Relation Extraction

Cause-effect relation extraction is the problem of detecting causal relations expressed in a text. The extraction of causal-relations from texts might be beneficial for the improvement of various natural language processing (NLP) tasks including Q/A, text-summarization, opinion mining, and event analysis. However, cause-effect relation in the text is sparse, ambiguous, sometimes implicit, and has a linguistically complex construct. To address these challenges FIRE-2020 introduced a shared task focusing on cause-effect relation extraction (CEREX). We propose a feature based supervised classification model with a naive rule-based classifier. We define a set of rules based on a causal connective dictionary and stop-words. Besides, we use a fusion of hand-crafted features and transfer learning embeddings to train our SVM based supervised classification model. Experimental results exhibit that our proposed method achieved the topnotch performance for cause-effect relation extraction and causal word annotation.