Determining similarity and inferring relations in a lexical knowledge base
Determining similarity and inferring relations in a lexical knowledge base
The analysis of noun sequences using semantic information extracted from on-line dictionaries
The analysis of noun sequences using semantic information extracted from on-line dictionaries
From a children's first dictionary to a lexical knowledge base of conceptual graphs
From a children's first dictionary to a lexical knowledge base of conceptual graphs
Kernel methods for relation extraction
The Journal of Machine Learning Research
REES: a large-scale relation and event extraction system
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
MindNet: acquiring and structuring semantic information from text
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Semantically significant patterns in dictionary definitions
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
Extracting semantic hierarchies from a large on-line dictionary
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
| Hi-index | 0.00 |
This paper presents an approach to construct rules automatically to identify semantic relations, such as Hypernym, Part_Of and Material from Modern Chinese Standard Dictionary (MCSD). This approach combines all the useful information such as part of speeches, syntactic information, and extracted semantic relations together in order to identify as many semantic relations as possible. However, not all the information provided, like syntactic information, is reliable. A method is required to ensure that the "right" information up to a point can be picked. And then all possible semantic relations are constructed as a net after being identified. Finally, we evaluate the net by determining word similarity.