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

International audience 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 stati...

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Bibliographic Details
Main Authors: Christodoulides, George, Avanzi, Mathieu, Goldman, Jean-Philippe
Other Authors: Institut Langage et Communication (ILC), Université Catholique de Louvain = Catholic University of Louvain (UCL), Université de Neuchâtel (UNINE), Université de Genève (UNIGE)
Format: Conference Object
Language:English
Published: HAL CCSD 2014
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Online Access:https://hal.archives-ouvertes.fr/hal-01703495
https://hal.archives-ouvertes.fr/hal-01703495/document
https://hal.archives-ouvertes.fr/hal-01703495/file/2014%20Christodoulides%20et%20al%20-%20DisMo.pdf
Description
Summary:International audience 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.