A benchmark for relation extraction kernels

Abstract

Relation extraction from textual documents is an important task in the context of information extraction. This task aims at identifying relations between pairs of named entities and assigning them a type. Relation extraction is often approached as a supervised classification problem, involving pre-processing steps such as text segmentation, entity recognition, and morphological and syntactic annotations. In previous studies, the way data is pre-processed differs among them, thus making the comparison of classification techniques for relation extraction unfair and inconclusive. Some of these classification techniques for relation extraction involve the use of kernels, which enable the comparison of complex structures. We propose a benchmark for the comparison of different kernels for relation extraction. Specifically, we propose the application of a common pre-processing stage, together with the use of an online learning algorithm to train Support Vector Machines with kernels designed for the classification of candidate pairs of related entities. We also report the results of the systematic experimental validation we have performed, using well known datasets in the area.