Distributional sentence representation by expert knowledge for causal relation identification

Extracting causal relations from natural sentences is an important issue in knowledge discovery. As a typical high level semantic problem with limited data, most systems only employ hand crafted features from various lexical semantic resources because it may generate very robust feature to support classification. However, human summarized knowledge is limited and there are more information in unlabeled corpora. To employ the features learned from unlabeled corpora, the authors propose a distributional sentence representation to make the distributional word representation applicable for high level semantic meaning problems. Experiments show that added features contain complementary knowledge for the causal relation expressions and it may improve the performance of the relation extraction system.