Combining Feature-based Kernel with Tree Kernel for Extracting Relations

Student thesis: Master thesis (including HD thesis)

  • Henrik Bayer Nielsen
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.
LanguageEnglish
Publication date6 Jun 2012
Number of pages40
ID: 63612158