Morphological Analysis as Classification: an Inductive-Learning Approach

TL;DR

This paper presents an inductive learning approach to morphological analysis, reformulating it as a segmentation task, demonstrating its effectiveness and advantages over traditional rule-based methods.

Contribution

It introduces a novel inductive learning method for morphological analysis as a classification task, showing its viability and benefits over traditional approaches.

Findings

Lazy learning algorithm IB1-IG performs best across tasks.

Inductive learning achieves good generalization in morphological analysis.

Approach is fast, deterministic, and language-independent.

Abstract

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm IB1-IG performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages over the traditional approach of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and processing, and being language-independent.