Semi-Supervised Convolution Graph Kernels for Relation Extraction

Extracting semantic relations between entities is an important step towards automatic text understanding. In this paper, we propose a novel Semi-supervised Convolution Graph Kernel (SCGK) method for semantic Relation Extraction (RE) from natural English text. By encoding sentences as dependency graphs of words, SCGK computes kernels (similarities) between sentences using a convolution strategy, i.e., calculating similarities over all possible short single paths on two dependency graphs. Furthermore, SCGK adds three semi-supervised strategies in the kernel calculation to enable soft-matching between (1) words, (2) grammatical dependencies, and (3) entire sentences, respectively. From a large unannotated corpus, these semi-supervision steps learn to capture contextual semantic patterns of elements inside natural sentences, and therefore alleviate the lack of annotated examples in most RE corpora. Through convolutions and multi-level semi-supervisions, SCGK provides a powerful model to e...

• Type: Journal
• Year: 2011
• Where: SDM
• Authors: Xia Ning, Yanjun Qi
• Keywords: Data Mining, Dependency Graphs, Natural Sentences, SDM 2011, Semantic Patterns