Semantic Relation Extraction With Kernels Over Typed Dependency Trees
published: Oct. 1, 2010, recorded: July 2010, views: 3756
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
An important step for understanding the semantic content of text is the extraction of semantic relations between entities in natural language documents. Automatic extraction techniques have to be able to identify different versions of the same relation which usually may be expressed in a great variety of ways. Therefore these techniques benefit from taking into account many syntactic and semantic features, especially parse trees generated by automatic sentence parsers. Typed dependency parse trees are edge and node labeled parse trees whose labels and topology contains valuable semantic clues. This information can be exploited for relation extraction by the use of kernels over structured data for classification. In this paper we present new tree kernels for relation extraction over typed dependency parse trees. On a public benchmark data set we are able to demonstrate a significant improvement in terms of relation extraction quality of our new kernels over other state-of-the-art kernels.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !