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- 575 creator gang-wang.
- 575 creator haiping-zhu.
- 575 creator yong-yu.
- 575 type InProceedings.
- 575 label "PORE: Positive-Only Relation Extraction from Wikipedia Text".
- 575 sameAs 575.
- 575 abstract "Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.".
- 575 hasAuthorList authorList.
- 575 hasTopic Information_extraction.
- 575 hasTopic Natural_language_processing.
- 575 hasTopic Semantic_Web.
- 575 hasTopic Web_annotation.
- 575 isPartOf proceedings.
- 575 title "PORE: Positive-Only Relation Extraction from Wikipedia Text".