  Distant supervision for relation extraction is an efficient method to reduce
labor costs and has been widely used to seek novel relational facts in large
corpora, which can be identified as a multi-instance multi-label problem.
However, existing distant supervision methods suffer from selecting important
words in the sentence and extracting valid sentences in the bag. Towards this
end, we propose a novel approach to address these problems in this paper.
Firstly, we propose a linear attenuation simulation to reflect the importance
of words in the sentence with respect to the distances between entities and
words. Secondly, we propose a non-independent and identically distributed
(non-IID) relevance embedding to capture the relevance of sentences in the bag.
Our method can not only capture complex information of words about hidden
relations, but also express the mutual information of instances in the bag.
Extensive experiments on a benchmark dataset have well-validated the
effectiveness of the proposed method.
