Creating Causal Embeddings for Question Answering with Minimal Supervision
Table 1: Number of causal tuples extracted from each corpus.
['Corpus', 'Extracted Tuples']
As bootstrapping methods are typically noisy, we manually evaluated the quality of approximately 250 of these pairs selected at random. Of the tuples evaluated, approximately 44% contained some amount of noise. For example, from the sentence Except for Springer’s show, which still relies heavily on confrontational topics that lead to fistfights virtually every day…, while ideally we would only extract (confrontational topics → fistfights), instead we extract the tuple (show which still relies heavily on confrontational topics → fistfights virtually every day), which contains a large amount of noise: show, relies, heavily, etc.