Selection and information: a class-based approach to lexical relationships
Selection and information: a class-based approach to lexical relationships
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Slot Grammar: A System for Simpler Construction of Practical Natural Language Grammars
Proceedings of the International Symposium on Natural Language and Logic
Kernel methods for relation extraction
The Journal of Machine Learning Research
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Identifying interesting assertions from the web
Proceedings of the 18th ACM conference on Information and knowledge management
Unsupervised discovery of domain-specific knowledge from text
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Using natural language to integrate, evaluate, and optimize extracted knowledge bases
Proceedings of the 2013 workshop on Automated knowledge base construction
| Hi-index | 0.00 |
One of the main bottlenecks in natural language processing is the lack of a comprehensive lexicalized relation resource that contains fine grained knowledge on predicates. In this paper, we present PRISMATIC, a large scale lexicalized relation resource that is automatically created over 30 gb of text. Specifically, we describe what kind of information is collected in PRISMATIC and how it compares with existing lexical resources. Our main focus has been on building the infrastructure and gathering the data. Although we are still in the early stages of applying PRISMATIC to a wide variety of applications, we believe the resource will be of tremendous value for AI researchers, and we discuss some of potential applications in this paper.