Kernel methods for relation extraction
The Journal of Machine Learning Research
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Relation extraction using label propagation based semi-supervised learning
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
Semi-supervised relation extraction with large-scale word clustering
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
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Relation extraction (RE) is the task of finding semantic relations between entities from text. As the supervised learning method requires a large amount of labeled training data, the semi-supervised learning method is the topics of interest. This paper presents a semi-supervised learning approach to relation extraction for Vietnamese text using bootstrapping. As the accuracy of syntactic parsing in Vietnamese text is still not high, we used Shallow Linguistic Kernel (SLK) which combines global kernel and local kernel to present sentences. The differences between our SLK and Giuliano et al.'s SLK [5] are: our global kernel not only use bags of words but also use part of speech, another entities type, a dictionary of compound verbs; The window size of right kernel of our local context starts from the beginning of the sentence to the word immediately before the second entity, the window size of left kernel start from the word immediately after the first entity to the end of the sentence. Our experimental results show that the supervised method using our SKL can achieve higher accuracy than the one used by Giuliano et al. [5]. And the system's accuracy when applying the bootstrapping method is higher than when applying the supervised one.