Exploring various knowledge in relation extraction
Institute for Infocomm Research · Agency for Science, Technology and Research
Abstract
Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based…
Citation impact
- FWCI
- 20.67
- Percentile
- 100%
- References
- 14
Authors
4- ZGZhou GuoDongCorresponding
Institute for Infocomm Research
- JSJian Su
Agency for Science, Technology and Research, Institute for Infocomm Research
- ZJZhang Jie
Agency for Science, Technology and Research, Institute for Infocomm Research
- ZMZhang Min
Institute for Infocomm Research
Topics & keywords
- Relationship extraction
- Computer science
- Chunking (psychology)
- Artificial intelligence
- Relation (database)
- WordNet
- Natural language processing
- Tree kernel