Alain Joubert

Integrating Knowledge-based Hypertext and Database for Task-oriented Access to Documents

Dealing with huge technical document bases is a very hot topic for high technology industry. We focus on a full integration of database, knowledge-based system and hypertext techniques to provide task-oriented-access to documents. An external semantic-driven structure is mapped onto documents owing to knowledge based hypertext techniques. A database is used to keep the elements describing this structure and to help access documents. The overall structure organization is specified by a task model. A hypertext-like interface to the document base is used to navigate within the task-organized information space. Links dynamically result from complex computed relationships handled by the knowledge representation system between concepts and from the concepts anchorage into the text. A medium scale prototype for a real document base about nuclear engineering has been developed. The paper describes the principle of integrating the three techniques and discusses the implementation.

Computing trees of named word usages from a crowdsourced lexical network

Thanks to the participation of a large number of persons via web-based games, a large-sized evolutionary lexical network is available for French. With this resource, we approached the question of the determination of the word usages of a term, and then we introduced the notion of similarity between these various word usages. So, we were able to build for a term its word usage tree: the root groups together all possible usages of this term and a search in the tree corresponds to a refinement of these word usages. The labelling of the various nodes of the word usage tree of a term is made during a width-first search: the root is labelled by the term itself and each node of the tree is labelled by a term stemming from the clique or quasi-clique this node represents. We show on a precise example that it is possible that some nodes of the tree, often leaves, cannot be labelled without ambiguity. This paper ends with an evaluation about word usages detected in our lexical network.

About Inferences in a Crowdsourced Lexical-Semantic Network

Automatically inferring new relations from already existing ones is a way to improve the quality of a lexical network by relation densification and error detection. In this paper, we devise such an approach for the JeuxDeMots lexical network, which is a freely avalaible lexical network for French. We first present deduction (generic to specific) and induction (specific to generic) which are two inference schemes ontologically founded. We then propose abduction as a third form of inference scheme, which exploits examples similar to a target term.

TOTAKI: A Help for Lexical Access on the TOT Problem

The JDM lexical network has been built thanks to on-line games the main of which, JeuxDeMots (JDM), was launched in 2007. It is currently a large lexical network, in constant evolution, containing more than 310,000 terms connected by more than 6.5 million relations. The riddle game Totaki (Tip Of the Tongue with Automated Knowledge Inferences), the initial version of which was elaborated with Michael Zock, was launched in a first version in 2010. The initial aim of this project is to cross validate the JDM lexical network. Totaki uses this lexical network to make proposals from user given clues, and in case of failure players can supply new information, hence enriching the network. Endogenous processes of inference, by deduction, induction, abduction, also allow to find new information not directly available in the network and hence lead to a densification of the network. The assumption about the validation is that if Totaki is able to guess proper terms from user clues, then the lexical network contains appropriate relations between words. Currently, Totaki achieves a 75 % success rate, to be compared to less than 50 % if the guessing is done by human users. One serious application of Totaki is to be viewed as a tool for lexical access and a possible remedy for the tip of the tongue problem. The Wikipedia encyclopaedia, built in a collaborative way, represents a very important volume of knowledge (about 1.5 million articles in its French version). The idea developed in this chapter consists in benefiting from Wikipedia to enrich the JDM network and evaluate the impact on Totaki performance. Instead of relying only on the JDM network, Totaki also makes use of information extracted from Wikipedia. The overall process is then both endogenous and exogenous. In a first part, we shall remind the reader the basic principles of a lexical network, then the aims and the underlying principles of the Totaki game. We shall see on examples Totaki may be used as a game to evaluate and enrich the JDM network, but also it may be considered as a tool for the Tip Of the Tongue problem; partial syntactic or morphologic information may be added to semantic information to help the user. In a second part, we shall show the results of the evaluation of the JDM network, results we obtained playing Totaki. We shall clarify the process allowing the introduction in the Totaki game of data extracted from Wikipedia as a complement in the information from the JDM network, and we shall briefly present the results provided by the first experiments.

Mixing Crowdsourcing and Graph Propagation to Build a Sentiment Lexicon: Feelings Are Contagious

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.