DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An Evaluation on a Corpus of French Spontaneous and Read Speech

TL;DR

DisMo is a multi-level annotator for spoken French that combines POS tagging, disfluency detection, and multi-word unit recognition using a hybrid system of rules, resources, and CRF models, achieving high accuracy on a diverse corpus.

Contribution

This paper introduces the first public version of DisMo for French, integrating multiple annotation levels and demonstrating high performance on spoken language data.

Findings

Achieves 95-96.8% accuracy in POS tagging of spoken French

Supports multi-level annotation including disfluencies and multi-word units

Effective across different varieties of spoken French from multiple countries

Abstract

We present DisMo, a multi-level annotator for spoken language corpora that integrates part-of-speech tagging with basic disfluency detection and annotation, and multi-word unit recognition. DisMo is a hybrid system that uses a combination of lexical resources, rules, and statistical models based on Conditional Random Fields (CRF). In this paper, we present the first public version of DisMo for French. The system is trained and its performance evaluated on a 57k-token corpus, including different varieties of French spoken in three countries (Belgium, France and Switzerland). DisMo supports a multi-level annotation scheme, in which the tokenisation to minimal word units is complemented with multi-word unit groupings (each having associated POS tags), as well as separate levels for annotating disfluencies and discourse phenomena. We present the system's architecture, linguistic resources and its hierarchical tag-set. Results show that DisMo achieves a precision of 95% (finest tag-set) to 96.8% (coarse tag-set) in POS-tagging non-punctuated, sound-aligned transcriptions of spoken French, while also offering substantial possibilities for automated multi-level annotation.