The Management of Context-Sensitive Features: A Review of Strategies

TL;DR

This paper reviews various heuristic strategies for managing context-sensitive features in supervised machine learning, highlighting methods for recovering implicit context and the potential benefits of hybrid approaches.

Contribution

It provides a comprehensive framework that encompasses existing techniques for context-sensitive learning, integrating multiple strategies and their synergies.

Findings

Hybrid strategies can have a synergistic effect.

Framework includes all known published techniques.

Methods for recovering lost contextual information are discussed.

Abstract

In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on contextsensitive learning.