Entity Relation Extraction Based on Entity Indicators

Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because sentence often contains several entity pairs, neural network is easily bewildered when learning relation representation without position and information about the considered pair. In this paper, instead of an abstract from raw inputs, task-related indicators are designed enable deep concentrate on task-relevant information. By implanting into instance, effective for encoding syntactic instance. Organized, structured unified can make similarity sentences that possess same or similar pair internal symmetry one more obviously. experiment, systemic analysis was conducted evaluate impact extraction. This method has achieved state-of-the-art performance, exceeding compared methods by than 3.7%, 5.0% 11.2% F1 score ACE Chinese corpus, English corpus literature text respectively.