A master thesis from Aalborg University
Combining Feature-based Kernel with Tree Kernel for Extracting Relations
Author(s)
Term
10. term
Publication year
2012
Submitted on
2012-06-06
Pages
40 pages
Abstract
This report proposes a composite kernel method of semantic rela- tion extraction between named entities within natural language docu- ments. Using two kernel methods, the benets from both are combined to gain an increase in performance compared to earlier approaches. 1) A linear kernel processing linguistic features, such as word-span, order- of-entities and word-type. 2) A tree kernel computing the similarity of a relation type with the shortest path-enclosed tree between a pair of candidate entities. Experiments are done using previous implemented methods, such as context sensitiveness and latent annotations to mea- sure their impact on the performance. Evaluating on a dataset for the relation extraction task at the Conference on Computational Natural Language Learning from 2004, the results obtained are on par with previous state-of-the-art approaches on the same dataset.
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.