MBT: A Memory-Based Part of Speech Tagger-Generator

TL;DR

This paper presents a memory-based method for part of speech tagging that automatically builds a tagger from a tagged corpus, offering comparable accuracy to statistical methods with advantages like small training data and fast processing.

Contribution

It introduces a memory-based approach using IGTree for efficient, scalable, and accurate part of speech tagging with dynamic context size determination.

Findings

Achieves tagging accuracy comparable to statistical methods.

Demonstrates feasibility of large-scale memory-based tagging.

Offers advantages like small training corpus and fast learning.

Abstract

We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.} The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed.