Subsequence Kernels for Relation Extraction
نویسندگان
چکیده
We present a new kernel method for extracting semantic relations between entities in natural language text, based on a generalization of subsequence kernels. This kernel uses three types of subsequence patterns that are typically employed in natural language to assert relationships between two entities. Experiments on extracting protein interactions from biomedical corpora and top-level relations from newspaper corpora demonstrate the advantages of this approach.
منابع مشابه
Exploiting graph kernels for high performance biomedical relation extraction
BACKGROUND Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Pa...
متن کاملSyntactic Tree-based Relation Extraction Using a Generalization of Collins and Duffy Convolution Tree Kernel
Relation extraction is a challenging task in natural language processing. Syntactic features are recently shown to be quite effective for relation extraction. In this paper, we generalize the state of the art syntactic convolution tree kernel introduced by Collins and Duffy. The proposed generalized kernel is more flexible and customizable, and can be conveniently utilized for systematic genera...
متن کاملExploiting Tree Kernels for High Performance Chemical Induced Disease Relation Extraction
Machine learning approaches based on supervised classification have emerged as effective methods for Biomedical relation extraction such as the Chemical-InducedDisease (CID) task. These approaches owe their success to a rich set of features crafted from the lexical and syntactic regularities in the text. Kernel methods are an effective alternative to manual feature engineering and have been suc...
متن کاملConvolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction
This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. We investigate the effectiveness of such representations in the automated relation extraction from text. We process the above data by mea...
متن کاملComposite Kernels For Relation Extraction
The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how different kernels for parse trees can be combined to improve the relation extraction quality. On a public benchmark dataset the combination of a kernel for phrase grammar parse trees and for dependency parse trees outperforms ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005