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This paper focuses on building up distributed representation of words in cause and effect spaces, a task-specific word embedding technique for causality. The causal embedding model is trained on a large set of cause-effect phrase pairs extracted from raw text corpus via a set of high-precision causal patterns. Three strategies are proposed to transfer the positive or negative labels from the level of phrase pairs to the level of word pairs, leading to three causal embedding models (Pairwise-Matching, Max-Matching, and AttentiveMatching) correspondingly. Experimental results have shown that Max-Matching and Attentive-Matching models significantly outperform several state-of-the-art competitors by a large margin on both English and Chinese corpora.

Distributed Representation of Words in Cause and Effect Spaces