RelExt

@article{Pingle2019RelExt,
  title={RelExt},
  author={Aditya Pingle and Aritran Piplai and Sudip Mittal and Anupam Joshi and James Holt and Richard Zak},
  journal={Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
  year={2019}
}
1 Citations

References

SHOWING 1-10 OF 10 REFERENCES
Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations
TLDR
This work employs Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text to obtain competitive results in the Automatic Content Extraction (ACE) evaluation.
Towards a Relation Extraction Framework for Cyber-Security Concepts
TLDR
A bootstrapping algorithm for extracting security entities and their relationships from text that requires little input data, specifically, a few relations or patterns, and incorporates an active learning component which queries the user on the most important decisions to prevent drifting from the desired relations.
Extracting Patterns and Relations from the World Wide Web
TLDR
This paper presents a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample and uses it to extract a relation of (author,title) pairs from the World Wide Web.
A translation approach to portable ontology specifications
TLDR
This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.
Web-scale information extraction in knowitall: (preliminary results)
TLDR
KnowItAll, a system that aims to automate the tedious process of extracting large collections of facts from the web in an autonomous, domain-independent, and scalable manner, is introduced.
Snowball: extracting relations from large plain-text collections
TLDR
This paper develops a scalable evaluation methodology and metrics for the task, and presents a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.
Discovering Relations among Named Entities from Large Corpora
TLDR
Using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision, but also that appropriate labels could be automatically provided for the relations.
URES : an Unsupervised Web Relation Extraction System
TLDR
URES (Unsupervised Relation Extraction System), which extracts relations from the Web in a totally unsupervised way and demonstrates that using simple noun phrase tagger is sufficient as a base for accurate patterns and compares the approach with KnowItAll's fixed generic patterns.
Extracting Relations with Integrated Information Using Kernel Methods
TLDR
This paper presents an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and shows that each level of syntactic processing contributes useful information for this task.