Étiquetage multilingue en parties du discours avec MElt

International audienceWe describe recent evolutions of MElt, a discriminative part-of-speech tagging system. MElt is targeted at the optimal exploitation of information provided by external lexicons for improving its performance over models trained solely on annotated corpora. We have trained MElt on more than 40 datasets covering over 30 languages. Compared with the state-of-the-art system MarMoT, MElt's results are slightly worse on average when no external lexicon is used, but slightly better when such resources are available, resulting in state-of-the-art taggers for a number of languages.Nous présentons des travaux récents réalisés autour de MElt, système discriminant d'étiquetage en parties du discours. MElt met l'accent sur l'exploitation optimale d'informations lexicales externes pour améliorer les performances des étiqueteurs par rapport aux modèles entraînés seulement sur des corpus annotés. Nous avons entraîné MElt sur plus d'une quarantaine de jeux de données couvrant plus d'une trentaine de langues. Comparé au système état-de-l'art MarMoT, MElt obtient en moyenne des résultats légèrement moins bons en l'absence de lexique externe, mais meilleurs lorsque de telles ressources sont disponibles, produisant ainsi des étiqueteurs état-de-l'art pour plusieurs langues

A game-based approach towards human augmented image annotation.

PhDImage annotation is a difficult task to achieve in an automated way. In this thesis, a human-augmented approach to tackle this problem is discussed and suitable strategies are derived to solve it. The proposed technique is inspired by human-based computation in what is called “human-augmented” processing to overcome limitations of fully automated technology for closing the semantic gap. The approach aims to exploit what millions of individual gamers are keen to do, i.e. enjoy computer games, while annotating media. In this thesis, the image annotation problem is tackled by a game based framework. This approach combines image processing and a game theoretic model to gather media annotations. Although the proposed model behaves similar to a single player game model, the underlying approach has been designed based on a two-player model which exploits the player’s contribution to the game and previously recorded players to improve annotations accuracy. In addition, the proposed framework is designed to predict the player’s intention through Markovian and Sequential Sampling inferences in order to detect cheating and improve annotation performances. Finally, the proposed techniques are comprehensively evaluated with three different image datasets and selected representative results are reported