  State-of-the-art methods for relation extraction consider the sentential
context by modeling the entire sentence. However, syntactic indicators, certain
phrases or words like prepositions that are more informative than other words
and may be beneficial for identifying semantic relations. Other approaches
using fixed text triggers capture such information but ignore the lexical
diversity. To leverage both syntactic indicators and sentential contexts, we
propose an indicator-aware approach for relation extraction. Firstly, we
extract syntactic indicators under the guidance of syntactic knowledge. Then we
construct a neural network to incorporate both syntactic indicators and the
entire sentences into better relation representations. By this way, the
proposed model alleviates the impact of noisy information from entire sentences
and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8
benchmark dataset show that our model significantly outperforms the
state-of-the-art methods.
