Abstract
Existing approaches to causal embeddings rely
heavily on hand-crafted high-precision causal patterns, leading to limited coverage. To solve this
problem, this paper proposes a method to boost
causal embeddings by exploring potential verbmediated causal patterns. It first constructs a seed
set of causal word pairs, then uses them as supervision to characterize the causal strengths of extracted verb-mediated patterns, and finally exploits
the weighted extractions by those verb-mediated
patterns in the construction of boosted causal embeddings. Experimental results have shown that
the boosted causal embeddings outperform several
state-of-the-arts significantly on both English and
Chinese. As by-products, the top-ranked patterns
coincide with human intuition about causality