Paper Info
Title
Open Relation Extraction Based on Core Dependency Phrase Clustering
Abstract
Relation extraction is very useful for many applications and has attracted much attention. The dominant prior methods for relation extraction were supervised methods which are relation-specific and limited by the availability of annotated training data. In this paper, we propose a method using hierarchical clustering to extract unbounded relations without relying on training data. The relation among entities in a sentence depends on the terms associated with the entities. Terms on the expandPath capture the relations between the entities. Given a relation, though an expandPath may have more than one dependency phrase, only the core dependency phrase describes the specific relation between the subject and the object. Our method uses heuristic rules to select the core dependency phrases and clusters entity pairs according to the similarity of the core dependency phrases in order to avoid irrelevant information and capture the semantics of the relation between entities more precisely. At last, our method automatically labels the relation clusters on basis of the semantics of core dependency phrases. The experimental results show that our method can cluster entity pairs which have the same relations more accurately and generate appropriate labels for the relations.
Year
DOI
Venue
2017
10.1109/DSC.2017.91
2017 IEEE Second International Conference on Data Science in Cyberspace (DSC)
Keywords
Field
DocType
relation extraction,expandPath,core dependency phrase,cluster,label
Hierarchical clustering,Multivalued dependency,Computer science,Phrase,Feature extraction,Natural language processing,Artificial intelligence,Cluster analysis,Sentence,Semantics,Relationship extraction
Conference
ISBN
Citations 
PageRank 
978-1-5386-1601-7
0
0.34
References 
Authors
22
5
Name
Order
Citations
PageRank
Chengsen Ru141.40
Shasha Li28520.31
Jintao Tang38914.00
Yi Gao429634.55
Ting Wang5369.43