  Extracting relations is critical for knowledge base completion and
construction in which distant supervised methods are widely used to extract
relational facts automatically with the existing knowledge bases. However, the
automatically constructed datasets comprise amounts of low-quality sentences
containing noisy words, which is neglected by current distant supervised
methods resulting in unacceptable precisions. To mitigate this problem, we
propose a novel word-level distant supervised approach for relation extraction.
We first build Sub-Tree Parse(STP) to remove noisy words that are irrelevant to
relations. Then we construct a neural network inputting the sub-tree while
applying the entity-wise attention to identify the important semantic features
of relational words in each instance. To make our model more robust against
noisy words, we initialize our network with a priori knowledge learned from the
relevant task of entity classification by transfer learning. We conduct
extensive experiments using the corpora of New York Times(NYT) and Freebase.
Experiments show that our approach is effective and improves the area of
Precision/Recall(PR) from 0.35 to 0.39 over the state-of-the-art work.
