Automatic rich annotation of large corpus of conversational transcribed speech

International audienceThis paper describes the use of the CasSys platform in order to achieve the chunking of conversational speech transcripts by means of cascades of Unitex transducers. Our system is involved in the EPAC project of the French National agency of Research (ANR). The aim of this project is to develop robust methods for the annotation of audio/multimedia document collections which contains conversational speech sequences such as TV or radio programs. At first, this paper presents the EPAC project and the adaptation of a former chunking system (Romus) which was developed in the restricted framework of dedicated spoken man-machine dialogue. Then, it describes the problems that are arising due to 1) spontaneous speech disfluencies and 2) errors for the previous stages of processing (automatic speech recognition and POS tagging)

Evaluation of a Grammar of French Determiners

Existing syntactic grammars of natural languages, even with a far from complete coverage, are complex objects. Assessments of the quality of parts of such grammars are useful for the validation of their construction. We evaluated the quality of a grammar of French determiners that takes the form of a recursive transition network. The result of the application of this local grammar gives deeper syntactic information than chunking or information available in treebanks. We performed the evaluation by comparison with a corpus independently annotated with information on determiners. We obtained 86% precision and 92% recall on text not tagged for parts of speech.Comment: 10 page

Improving a symbolic parser through partially supervised learning

International audienceRecently, several statistical parsers have been trained and evaluated on the dependency version of the French TreeBank (FTB). However, older symbolic parsers still exist, including FRMG, a wide coverage TAG parser. It is interesting to compare these different parsers, based on very different approaches, and explore the possibilities of hybridization. In particular, we explore the use of partially supervised learning techniques to improve the performances of FRMG to the levels reached by the statistical parsers.Récemment, plusieurs analyseurs syntaxiques statistiques ont été entrainés et évalués sur la version en dépendances du French TreeBank (FTB). Cependant, des analyseurs symboliques plus anciens continuent à exister, dont FRMG, un analyseur TAG à large couverture. Il est intéressant de comparer ces divers analyseurs, fondés sur des approches très différentes et d'explorer des possibilités d'hybridation. En particulier, nous explorons l'utilisation de techniques d'apprentissage partiellement supervisé pour améliorer les performances de FRMG au niveau de celles des analyseurs statistiques

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition