Tree Kernels for semantic role labeling

Tree Kernels for semantic role labeling (32 pages)

0.   Abstract

This paper is based on kernel functions to model parse tree properties in kernel based machines.

1.   Introduction

Recently attention: automatic labeling of semantic roles. It requires:

1. Detection of the target predicate

2. Detection and classification of constituting arguments.

Previous work:

-          Machine learning

-          Linking theories semantic and syntax

Tree kernel for:

-          Parsing re-ranking

-          Relation extraction

-          Name entity recognition.

Recent work with high labeling accuracy,

Joint inference on predicate-argument structure. For this -> extract features from sentence syntactic parse tree.

1-      Feature extraction.

2-      Combination with traditional attribute value models.

3-      Feature engineering using kernel combinations and marking strategies.

4-      Tree kernels….

5-      Identify the most important structured features: highest-weighted features -> better classifiers.

“Tree kernels should always be applied”.

Limitations:

a)      Poor impact on boundary detection.

b)      Limited contribution to overall accuracy.

Tree kernels for srl: types:

-          Canonical mappings

-          Feature extraction functions

Carefully engineered tree kernels always increase accuracy.

 

2.   Automatic shallow semantic parsing

3.   Tree kernels

4.   State of the art architecture for SRL

5.   Structured feature engineering

6.   Experiments

7.   Discursion and conclusions