DocumentCode :
134213
Title :
A bottom-up kernel of pattern learning for relation extraction
Author :
Chunyun Zhang ; Weiran Xu ; Sheng Gao ; Jun Guo
Author_Institution :
Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2014
fDate :
12-14 Sept. 2014
Firstpage :
609
Lastpage :
613
Abstract :
Measuring the similarity of patterns is the key in pattern-based approaches in relation extraction. Most existing methods generally rely on inflexible pattern similarity measurements which often lead to low recall. In this work, a novel kernel-based model is proposed to address this problem. Depending on the pattern similarities produced by our bottom-up kernel, the most similar semantic shortest dependency patterns are selected to update seed patterns in each iteration of bootstrapping. To obtain insights of the reliability and applicability of our proposed method, we applied it to the task of English Slot Filling (ESF) in Knowledge Base Population (KBP) track at Text Analysis Conference (TAC). The experimental results validate our proposed method that importantly improves the recall which resulting in the improvement of F1 value. The effectiveness of the bottom-up kernel is also verified by further experimental results.
Keywords :
iterative methods; knowledge based systems; learning (artificial intelligence); natural language processing; statistical analysis; text analysis; ESF; English slot filling; F1 value; KBP; TAC; bootstrapping iteration; bottom-up kernel; inflexible pattern similarity measurements; kernel-based model; knowledge base population; pattern learning; pattern-based approaches; relation extraction; text analysis conference; Computational linguistics; Computer architecture; Feature extraction; Kernel; Natural language processing; Pattern matching; Semantics; kernel; natural language processing; relation extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Chinese Spoken Language Processing (ISCSLP), 2014 9th International Symposium on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/ISCSLP.2014.6936605
Filename :
6936605
Link To Document :
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