A Hybrid Method for Chinese Entity Relation Extraction

Entity relation extraction is an important task for information extraction, which refers to extracting the relation between two entities from input text. Previous researches usually converted this problem to a sequence labeling problem and used statistical models such as conditional random field model to solve it. This kind of method needs a large, high-quality training dataset. So it has two main drawbacks: 1) for some target relations, it is not difficult to get training instances, but the quality is poor; 2) for some other relations, it is hardly to get enough training data automatically. In this paper, we propose a hybrid method to overcome the shortcomings. To solve the first drawback, we design an improved candidate sentences selecting method which can find out high-quality training instances, and then use them to train our extracting model. To solve the second drawback, we produce heuristic rules to extract entity relations. In the experiment, the candidate sentences selecting method improves the average F1 value by 78.53% and some detailed suggestions are given. And we submitted 364944 triples with the precision rate of 46.3% for the competition of Sougou Chinese entity relation extraction and rank the 4th place in the platform.