Abstract
Relation extraction is to discover relations between entities mentioned in the plain text. It can be used to generate semantic data in form of RDF triples representing facts. In this paper, we focus on relation extraction from Chinese text, which is less studied compared with that for English. Chinese words and phrases have great ambiguities on syntax and semantic. Thus, Chinese NLP tools can be insufficient when the sentence is too long or the sentence structure is too complex. Unfortunately, this is the case in the real world data. In order to tackle the limitation of the current Chinese NLP tools, we propose a method called sentence rolling to generate several enhanced inputs from the original input to help generate the correct relation candidates. In order to rank these candidates in an appropriate way, a voting approach is applied based on several statistic-based ranking function. Further, a Relation KB is used to help determine the subject part and the object part for the selected relation candidate. We carried out comprehensive experiments on both real world news corpus and benchmark data combining Chinese Treebank and Chinese Dependency Treebank. The experimental results show that the method can improve the performance of relation extraction significantly compared with the existing ones and cost a reasonable time.
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Sheng, M., Qiu, L., Wu, C., Wang, H., Yu, Y. (2013). Effective Chinese Relation Extraction by Sentence Rolling and Candidate Ranking. In: Qi, G., Tang, J., Du, J., Pan, J.Z., Yu, Y. (eds) Linked Data and Knowledge Graph. CSWS 2013. Communications in Computer and Information Science, vol 406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54025-7_13
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DOI: https://doi.org/10.1007/978-3-642-54025-7_13
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