Ilaine Wang


2020

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ODIL_Syntax: a Free Spontaneous Spoken French Treebank Annotated with Constituent Trees
Ilaine Wang | Aurore Pelletier | Jean-Yves Antoine | Anaïs Halftermeyer
Proceedings of the 12th Language Resources and Evaluation Conference

This paper describes ODIL Syntax, a French treebank built on spontaneous speech transcripts. The syntactic structure of every speech turn is represented by constituent trees, through a procedure which combines an automatic annotation provided by a parser (here, the Stanford Parser) and a manual revision. ODIL Syntax respects the annotation scheme designed for the French TreeBank (FTB), with the addition of some annotation guidelines that aims at representing specific features of the spoken language such as speech disfluencies. The corpus will be freely distributed by January 2020 under a Creative Commons licence. It will ground a further semantic enrichment dedicated to the representation of temporal entities and temporal relations, as a second phase of the ODIL@Temporal project. The paper details the annotation scheme we followed with a emphasis on the representation of speech disfluencies. We then present the annotation procedure that was carried out on the Contemplata annotation platform. In the last section, we provide some distributional characteristics of the annotated corpus (POS distribution, multiword expressions).

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Contemplata, a Free Platform for Constituency Treebank Annotation
Jakub Waszczuk | Ilaine Wang | Jean-Yves Antoine | Anaïs Halftermeyer
Proceedings of the 12th Language Resources and Evaluation Conference

This paper describes Contemplata, an annotation platform that offers a generic solution for treebank building as well as treebank enrichment with relations between syntactic nodes. Contemplata is dedicated to the annotation of constituency trees. The framework includes support for syntactic parsers, which provide automatic annotations to be manually revised. The balanced strategy of annotation between automatic parsing and manual revision allows to reduce the annotator workload, which favours data reliability. The paper presents the software architecture of Contemplata, describes its practical use and eventually gives two examples of annotation projects that were conducted on the platform.

2016

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From built examples to attested examples: a syntax-based query for non-specialists
Ilaine Wang | Sylvain Kahane | Isabelle Tellier
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters

2014

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Can we chunk well with bad POS labels? (Peut-on bien chunker avec de mauvaises étiquettes POS ?) [in French]
Isabelle Tellier | Iris Eshkol-Taravella | Yoann Dupont | Ilaine Wang
Proceedings of TALN 2014 (Volume 1: Long Papers)

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Macrosyntactic Segmenters of a French Spoken Corpus
Ilaine Wang | Sylvain Kahane | Isabelle Tellier
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The aim of this paper is to describe an automated process to segment spoken French transcribed data into macrosyntactic units. While sentences are delimited by punctuation marks for written data, there is no obvious hint nor limit to major units for speech. As a reference, we used the manual annotation of macrosyntactic units based on illocutionary as well as syntactic criteria and developed for the Rhapsodie corpus, a 33.000 words prosodic and syntactic treebank. Our segmenters were built using machine learning methods as supervised classifiers~: segmentation is about identifying the boundaries of units, which amounts to classifying each interword space. We trained six different models on Rhapsodie using different sets of features, including prosodic and morphosyntactic cues, on the assumption that their combination would be relevant for the task. Both types of cues could be resulting either from manual annotation/correction or from fully automated processes, which comparison might help determine the cost of manual effort, especially for the 3M words of spoken French of the Orfeo project those experiments are contributing to.