Tagging French -- comparing a statistical and a constraint-based method

TL;DR

This paper compares statistical and constraint-based part-of-speech tagging methods for French, showing that the constraint-based approach outperforms the statistical one within a similar development time frame.

Contribution

It provides a comparative analysis of two different tagging approaches for French, highlighting the efficiency of constraint-based methods with limited development time.

Findings

Constraint-based tagger outperforms statistical tagger within same development time

Statistical method achieves accuracy comparable to English taggers

Constraint-based approach is more effective with limited rule development time

Abstract

In this paper we compare two competing approaches to part-of-speech tagging, statistical and constraint-based disambiguation, using French as our test language. We imposed a time limit on our experiment: the amount of time spent on the design of our constraint system was about the same as the time we used to train and test the easy-to-implement statistical model. We describe the two systems and compare the results. The accuracy of the statistical method is reasonably good, comparable to taggers for English. But the constraint-based tagger seems to be superior even with the limited time we allowed ourselves for rule development.