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Extracting semantic relations from texts is a good way to build and supply a knowledge base, an indispensable resource for text analysis. We propose and evaluate the combination of three ways of producing lexical-semantic relations.

International audience; Correcting errors in a data set is a critical issue. This task can be either handmade by experts, or by crowdsourcing methods or automatically done using algorithms. Although even if the rate of errors present in a given lexical network is rather low, it is important to reduce it. We present here automatic methods for detecting potential secondary errors that would result from automatic inference mechanisms when they rely on an initial error manually detected. Encouraging results also invite us to consider strategies that would automatically detect "erroneous" initial relations, which could lead to the automatic detection of the majority of errors in a lexical-semantic network.

This paper describes a method for building a sentiment lexicon. Its originality is to combine crowdsourcing via a Game With A Purpose (GWAP) with automated propagation of sentiments through a spreading algorithm, both using the lexical JeuxDeMots network as data source and substratum. We present the game designed to collect sentiment data, and the principles and assumptions underlying the action of the algorithm that propagates them within the network. Finally, we give a qualitative evaluation of the data obtained for both the game and the spreading done by the algorithm.

International audience; In Natural Language Processing and semantic analysis in particular, color information may be important for processing textual information. Knowing what colors are generally associated with terms by people is valuable. We explore how crowdsourcing through a game with a purpose (GWAP) can be an adequate strategy to collect such lexico-semantic data.