Dans cet article, nous présentons la mise en œuvre d’une chaîne de traitement sémantique complète dédiée aux conversations audio issues de centres d’appel téléphoniques, depuis la phase de transcription automatique jusqu’à l’exploitation des résultats, en passant par l’étape d’analyse sémantique des énoncés. Nous décrivons ici le fonctionnement des différentes analyses que notre équipe développe, ainsi que la plateforme interactive permettant de restituer les résultats agrégés de toutes les conversations analysées.

Cet article décrit les systèmes de l’équipe Eloquant pour la catégorisation de tweets en français dans les tâches 1 (détection de la thématique transports en commun) et 2 (détection de la polarité globale) du DEFT 2018. Nos systèmes reposent sur un enrichissement sémantique, l’apprentissage automatique et, pour la tâche 1 une approche symbolique. Nous avons effectué deux runs pour chacune des tâches. Nos meilleures F-mesures (0.897 pour la tâche 1 et 0.800 pour la tâche 2) sont au-dessus de la moyenne globale pour chaque tâche, et nous placent dans les 30% supérieurs de tous les runs pour la tâche 2.

In this paper we present the results of an ongoing experiment of bootstrapping a Treebank for Catalan by using a Dependency Parser trained with Spanish sentences. In order to save time and cost, our approach was to profit from the typological similarities between Catalan and Spanish to create a first Catalan data set quickly by automatically: (i) annotating with a de-lexicalized Spanish parser, (ii) manually correcting the parses, and (iii) using the Catalan corrected sentences to train a Catalan parser. The results showed that the number of parsed sentences required to train a Catalan parser is about 1000 that were achieved in 4 months, with 2 annotators.

Distributional thesauri have been applied for a variety of tasks involving semantic relatedness. In this paper, we investigate the impact of three parameters: similarity measures, frequency thresholds and association scores. We focus on the robustness and stability of the resulting thesauri, measuring inter-thesaurus agreement when testing different parameter values. The results obtained show that low-frequency thresholds affect thesaurus quality more than similarity measures, with more agreement found for increasing thresholds. These results indicate the sensitivity of distributional thesauri to frequency. Nonetheless, the observed differences do not transpose over extrinsic evaluation using TOEFL-like questions. While this may be specific to the task, we argue that a careful examination of the stability of distributional resources prior to application is needed.

The work we present here addresses cue-based noun classification in English and Spanish. Its main objective is to automatically acquire lexical semantic information by classifying nouns into previously known noun lexical classes. This is achieved by using particular aspects of linguistic contexts as cues that identify a specific lexical class. Here we concentrate on the task of identifying such cues and the theoretical background that allows for an assessment of the complexity of the task. The results show that, despite of the a-priori complexity of the task, cue-based classification is a useful tool in the automatic acquisition of lexical semantic classes.

This paper describes version 1.3 of the FreeLing suite of NLP tools. FreeLing was first released in February 2004 providing morphological analysis and PoS tagging for Catalan, Spanish, and English. From then on, the package has been improved and enlarged to cover more languages (i.e. Italian and Galician) and offer more services: Named entity recognition and classification, chunking, dependency parsing, and WordNet based semantic annotation. FreeLing is not conceived as end-user oriented tool, but as library on top of which powerful NLP applications can be developed. Nevertheless, sample interface programs are provided, which can be straightforwardly used as fast, flexible, and efficient corpus processing tools. A remarkable feature of FreeLing is that it is distributed under a free-software LGPL license, thus enabling any developer to adapt the package to his needs in order to get the most suitable behaviour for the application being developed.