Extracting Relation Descriptors with Conditional Random Fields

In this paper we study a novel relation extraction problem where a general relation type is defined but relation extraction involves extracting specific relation descriptors from text. This new task can be treated as a sequence labeling problem. Although linear-chain conditional random fields (CRFs) can be used to solve this problem, we modify this baseline solution in order to better fit our task. We propose two modifications to linear-chain CRFs, namely, reducing the space of possible label sequences and introducing long-range features. Both modifications are based on some special properties of our task. Using two data sets we have annotated, we evaluate our methods and find that both modifications to linear-chain CRFs can significantly improve the performance for our task.