Fabrice Evrard | ENSEEIHT Toulouse (original) (raw)
Papers by Fabrice Evrard
This document is distributed in the following chunks: 1. Illustrates the theoretical bases of the... more This document is distributed in the following chunks: 1. Illustrates the theoretical bases of the project. It is necessary to demostrate that decisions have foundations. Chapter 2. State of the art Planning and Anticipation are taken into account. In the first case, several methods have been investigated to see whether they are feasible to implement. The other, it has been necessary to search for basic theory. Not algorithms, but rather basic theory as it is not easy in this field. An other interesting point, it's about to enumerate the challenges that represents each point. 1.4. SUMMARY 11 2. All project details are explained. From the conception to the implementation phase. However, there is a small introduction to SACECA. Chapter 3. SACECA platform Brief introduction to SACECA project's terms. It is necessary to understand them to follow the next chapters. Chapter 4. Planning under SACECA It is necessary to explain the basis to justify the new planning. This way, it analyzes the current SACECA algorithm. Because once determinated the weak points, it is going to justify the new implementation. The other goal is to integrate Prolog with SACECA. For this reason, it explains in detail how it has been done and which guidelines has followed. Chapter 5. Anticipation under SACECA Like the chaper above, anticipation must be jusfified with theory and questions like, what, how and why must be answered. UML diagram classes have been added in order to increse the comprehension, because they help to represent arquitecture and design. Chapter 6. Anticipation experiments under Planning A part from implementation, results must be validated. It's required to test the software to assure it works and goals are achieved. Chapter 7. Project Planning To make the project, it's set a series of dates indicating when the project phases begin and end. It is also done an assessment about of what really happen with project itself. 3. Those chapters take into account different perspectives and impressions about the project. In the last one, it needs to objective. Chapter 8. Perspectives The months dedicated to the project gives you, at the end, a deep perspective about new ideas for future iterations. As well as, to do it in other way because some ideas where not good. Chapter 9. Conclusion Exposes a balance about goals. If they have been accomplished or not. It also gives personal impressions about it. 4. The document also provides several appendices that shows the final code implemented in SACECA. The majority of them are coded in Java except for the last ones which It's coded in Prolog. Appendices AN. Java code Include all classes implemented in Java. Each appendix represents a Java package. It's worth to say there are so many planning methods. En this section, several options are explained and analyzed to know which one could be useful to, at the end, pick one. However, it's necessary to define to which problem faces SACECA. That way, it'd be possible to restrict several options and focus in those ones which, beforehand they could be more feasible. 2.3.1 Problem Classification As it's the brief statement says, an inhabitant of SACECA lives in a simple environment, not like ours. So when you plan some tasks, you are sure if you will succeed in 100% because there are no independent events neither an unexpected effects on you. As well as, actions can not fail. Also, do not exists external events which they have no potential to modify our original plan. Finally, there is a full view of the world around us. That said, now we can theoretically affirm that this problem requires to apply a classical algorithm. At this point we already know to what kind of problem we're facing. So now we need to look in a more detailed way because is a statement requirement. 2.3.2 Methods [6] [11] The following options are presented as possible solutions for planning. They all solve the classical problem types. Mes concretament, exploren l'espai d'estats del problema a partir d'un estat inicial fins a trobar l'objectiu designat. 2.4. THE PROBLEM OF ANTICIPATION INTEGRATION UNDER PLANNING 21 Forward Chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence) and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. D'alta banda, aquest metode tendeix a explorar totes les opcions, fins i tot aquelles que no son pertinents. Aixo el fa poc eficaç quan l'espai a explorar es enorme. Backward Chaining is an inference method that can be described (in lay terms) as working backward from the goal(s). It is used in automated theorem provers, proof assistants and other artificial intelligence applications, but it has also been observed in primates. Heuristics under Planning Forward and backward chaining no son eficaços sense una bona funcio heuristica, el qual es dedica a evaluar la distancia entre un estat s i l'objectiu final. Pero per definir-lo es necessari que un individu huma el dissenyi i l'adapti al problema en questio. Partial-Order Planning Forward and Backward State-Space Search Solution path imposes total ordering on actions in a plan. e.g. do X then Y then Z ... (a sequence of actions). But with Partial-Order Planning, solutions define multiple action orderings (partial ordering: a set of sequences achieving goals). Allows agent to use alternate concrete plan if an action becomes unavailable. Tackle subgoals independently, merge resulting plans achieved by searching in a space of partially-ordered plans, rather than states of the environment. 2.4 The Problem of Anticipation Integration under Planning Anticipation it's already itself a big challenge. Even whether it's basic. But in addition, to integrate with planning it's even worse given the SACECA. The next points explain different problem which the project could face while working on it: • Small testing area: SACECA doesn't have a lot of objects to be tested. There are gauges but not with different options. Apparently, it can difficult to demonstrate that works for all cases. • Modifications on SACECA project: can be necessary to change SACECA skeleton to add anticipation. Which could lead to a difficult implementation. Chapter 3 SACECA Platform This chapter describes which SACECA parameters are necessary to know. Because they'll conditionate planning and anticipation. Avatar this concept does reference to SACECA inhabitants. They represent a person. Goal defines which objective must satisfy the avatar. So, planning algorithm must obey this Goal. Gauge avatars have several gauges to satisfy, otherwise they would die. At the time of the project there are four: Hunger, Tiredness, Happiness and Thirsty. It's an integer number which ranges from 0 to 100. In addition, it has a threshold that when Gauge value underpass it, a Goal is created to satisfy Gauge. It decreases depending on each Avatar. It's easy to know how it works. In a nutshell, every X clock "ticks" the value decreases by one unit. Services every object found in SACECA can offer a service. Those services let an avatar to satisfy their needs directly or indirectly-intermediate steps-. Some services have prerequisites that they must be accomplished before execution. For example: have enough money to buy a can. Properties avatar have properties which define them. Those values are dynamic. They have a name, a type and a default value. DefaultPlanningModule.java this file contains the first planning algorithm used which used SACECA in the first version. In the next part, It's explained carefully. CHAPTER 4. PLANNING UNDER SACECA 4.1.5 Related functions The purpose of this part is to describe what the most important functions do. As the idea is to help to improve the comprehension of the DFS algorithm in SACECA. Goal: seemsReached(JexlContent) This function evaluates the agents goals with the help of Jexl library. Those goals are defined by object service conditions. if (!goal.seemsReached(new MemoryAwareU3duJexlContext(agent))) Service: isUsable(object, clonedAgentMemory, null) As it's name says, according to agent (important) memory, it tries to know if it's possible to use the desired object. This means the agent satisfies the preconditions imposed by the service. For example, it has enough money to buy a can. handleWalkTo(object, clonedAgent, service.getMaxDistanceForUsage()) Imagine that given an object, an agent and a maximum distance we try to figure out if the place is reachable. As it has been said before, the object could be surrounded by an other. Which it doesn't let you use it. Finally, it returns a PlanElement or null. service: execute(object, clonedAgentMemory, null, ExecutionMode.VIRTUAL) Giving the object and the agents memory it applies the effects of a service on the agent. For example, if the agents goes to a restaurant, the amount of money have to be reduced and his hunger bar set to maximum. The Virtual execution mode only modifies the agents memory. In normal way, the agent would execute it in real life. 4.1.6 Detailed Example A complete example is provided to give a better comprehension. So, the next chapter is a complete explanation of what planning involves in SACECA. Therefore, to provide an example it's necessary to define which properties and data have the agent, as well as what kind of actions can be performed. 4.1.6.1 Initial Data Those properties can be found in SACECA application, in Memory tab. After that, it requires to search Me field. Agent properties defined by SACECA are shown there.
HAL (Le Centre pour la Communication Scientifique Directe), 2006
G.-L. Baron, J. Baudé & B. de La …, 1993
We study in this paper the impact of Word Sense Disambiguation (WSD) on Query Expansion (QE) for ... more We study in this paper the impact of Word Sense Disambiguation (WSD) on Query Expansion (QE) for monolingual intelligent information retrieval. The proposed approaches for WSD and QE are based on corpus analysis using co-occurrence graphs modelled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantages of a double measure (possibility and necessity). Our experiments are performed using the standard ROMANSEVAL test collection for the WSD task and the CLEF-2003 benchmark for the QE process in French monolingual Information Retrieval (IR) evaluation. The results show the positive impact of WSD on QE based on the recall/precision standard metrics.
International Conference on Information and Knowledge Engineering, 2007
Proc. 9th Symposium on Languages for special Purposes. Bergen (Norway), 1993
The Intelligent Dictionary Help System (IDHS) is a monolingual (explanatory) dictionary system.[1... more The Intelligent Dictionary Help System (IDHS) is a monolingual (explanatory) dictionary system.[1–4] Its design was conceived from the study of questions that human users would like to have answered when consulting a dictionary. The fact that it is intended for people instead of automatic processing distinguishes it from other systems dealing with the acquisition of semantic knowledge from conventional dictionaries. The system provides various access possibilities to the data, allowing the deduction of implicit knowledge from ...
This paper presents a web information retrieval system based on Hierarchical Small-Worlds (HSW) a... more This paper presents a web information retrieval system based on Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). The first HSW consists in structuring the "Google" search results in dense zones of web pages which strongly depend on each other. We thus reveal dense clouds of pages which "speak" more or less about the same subject and which all strongly answer the user’s query. The goal of the second HSW consists in considering the query as multiple in the sense that we don’t seek only the keyword in the web pages but also its synonyms. The PN generates the mixing of these two HSW in order to organize the searched documents according to user’s preferences. Indeed, SARIPOD is a new approach for Information Retrieval Model based on possibility and necessity measures. This model encodes relationship dependencies existing between query terms and web documents through naïve possibilistic networks and quantifies these relationships by two measures: possib...
Procesamiento Del Lenguaje Natural, 1991
DIAC, diccionario inteligente de ayuda a la comprensión, se ha concebido como un sistema computac... more DIAC, diccionario inteligente de ayuda a la comprensión, se ha concebido como un sistema computacional para facilitar y enriquecer la utilización por parte de un usuario humano de la información contenida en un diccionario monolingüe. El propio sistema está dotado de capacidad de deducción, de forma que pueda responder a solicitudes no triviales del usuario. DIAC se está desarrollando a partir de una Base de Datos Diccionarial (BDD), cuyo contenido se ha analizado para estructurar una Base de Conocimientos (BCD), mediante la especificación de la semántica asociada a las diversas estructuras definitorias. Esta BCD es el soporte sobre el que estamos desarrollando el sistema de explotación "inteligente". Para ello es preciso determinar y especificar cuáles son las necesidades del usuario a las que habrá que responder. Desde este punto de vista la BCD se concibe como una estructura de Tipos Abstractos de Datos caracterizada por un conjunto de operaciones. En el presente artícu...
![Research paper thumbnail of Evaluation of a possibilistic classification approach for Arabic texts disambiguation (Evaluation d’une approche de classification possibiliste pour la désambiguïsation des textes arabes) [in French]](https://attachments.academia-assets.com/78858113/thumbnails/1.jpg)
](Morphological disambiguation of Arabic words consists in identifying their appropriate morphologi... more Morphological disambiguation of Arabic words consists in identifying their appropriate morphological analysis. In this paper, we present three models of morphological disambiguation of non-vocalized Arabic texts based on possibilistic classification. This approach deals with imprecise training and testing datasets, as we learn from untagged texts. We experiment our approach on two corpora i.e. the Hadith corpus and the Arabic Treebank. These corpora contain data of different types: traditional and modern. We compare our models to probabilistic and statistical classifiers. To do this, we transform the structure of the training and the test sets to deal with imprecise data. Mots-clés : Traitement Automatique des Langues Naturelles, Désambiguïsation Morphologique de l’Arabe, Théorie des Possibilités, Classification Possibiliste.
This paper presents an Internet information retrieval system based on Hierarchical Small-Worlds (... more This paper presents an Internet information retrieval system based on Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). The first HSW, for the words of the French language, is used to take account of the dependences between these words. The second HSW is devoted to the web pages required and translated in the same way the dependences between these pages. The PN generates the mixing of these two HSW in order to organize the sershed documents according to the user profile. Our system propose a mixed approach for Information Retrieval Model based on possibility and necessity measures. This model encodes relationship dependencies existing between query terms and web documents through naïve possibilistic networks and quantifies these relationships by two measures: possibility and necessity. The retrieved documents are those which are necessarily or possibly relevant given a user's query. The search process restores the plausibly or necessarily relevant documents for a ...
This paper presents and experiments a new approach for automatic word sense disambiguation (WSD) ... more This paper presents and experiments a new approach for automatic word sense disambiguation (WSD) applied for French texts. First, we are inspired from possibility theory by taking advantage of a double relevance measure (possibility and necessity) between words and their contexts. Second, we propose, analyze and compare two different training methods: judgment and dictionary based training. Third, we summarize and discuss the overall performance of the various performed tests in a global analysis way. In order to assess and compare our approach with similar WSD systems we performed experiments on the standard ROMANSEVAL test collection.
International Journal on Knowledge and Information Systems. 01/2014;
Applications of Artificial Intelligence in Engineering VI, 1991
Knowledge and Information Systems, 2014
This paper proposes and assesses a new possibilistic approach for automatic monolingual word sens... more This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems.
Knowledge and Information Systems, 2014
This paper proposes and assesses a new possibilistic approach for automatic monolingual word sens... more This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems.
This document is distributed in the following chunks: 1. Illustrates the theoretical bases of the... more This document is distributed in the following chunks: 1. Illustrates the theoretical bases of the project. It is necessary to demostrate that decisions have foundations. Chapter 2. State of the art Planning and Anticipation are taken into account. In the first case, several methods have been investigated to see whether they are feasible to implement. The other, it has been necessary to search for basic theory. Not algorithms, but rather basic theory as it is not easy in this field. An other interesting point, it's about to enumerate the challenges that represents each point. 1.4. SUMMARY 11 2. All project details are explained. From the conception to the implementation phase. However, there is a small introduction to SACECA. Chapter 3. SACECA platform Brief introduction to SACECA project's terms. It is necessary to understand them to follow the next chapters. Chapter 4. Planning under SACECA It is necessary to explain the basis to justify the new planning. This way, it analyzes the current SACECA algorithm. Because once determinated the weak points, it is going to justify the new implementation. The other goal is to integrate Prolog with SACECA. For this reason, it explains in detail how it has been done and which guidelines has followed. Chapter 5. Anticipation under SACECA Like the chaper above, anticipation must be jusfified with theory and questions like, what, how and why must be answered. UML diagram classes have been added in order to increse the comprehension, because they help to represent arquitecture and design. Chapter 6. Anticipation experiments under Planning A part from implementation, results must be validated. It's required to test the software to assure it works and goals are achieved. Chapter 7. Project Planning To make the project, it's set a series of dates indicating when the project phases begin and end. It is also done an assessment about of what really happen with project itself. 3. Those chapters take into account different perspectives and impressions about the project. In the last one, it needs to objective. Chapter 8. Perspectives The months dedicated to the project gives you, at the end, a deep perspective about new ideas for future iterations. As well as, to do it in other way because some ideas where not good. Chapter 9. Conclusion Exposes a balance about goals. If they have been accomplished or not. It also gives personal impressions about it. 4. The document also provides several appendices that shows the final code implemented in SACECA. The majority of them are coded in Java except for the last ones which It's coded in Prolog. Appendices AN. Java code Include all classes implemented in Java. Each appendix represents a Java package. It's worth to say there are so many planning methods. En this section, several options are explained and analyzed to know which one could be useful to, at the end, pick one. However, it's necessary to define to which problem faces SACECA. That way, it'd be possible to restrict several options and focus in those ones which, beforehand they could be more feasible. 2.3.1 Problem Classification As it's the brief statement says, an inhabitant of SACECA lives in a simple environment, not like ours. So when you plan some tasks, you are sure if you will succeed in 100% because there are no independent events neither an unexpected effects on you. As well as, actions can not fail. Also, do not exists external events which they have no potential to modify our original plan. Finally, there is a full view of the world around us. That said, now we can theoretically affirm that this problem requires to apply a classical algorithm. At this point we already know to what kind of problem we're facing. So now we need to look in a more detailed way because is a statement requirement. 2.3.2 Methods [6] [11] The following options are presented as possible solutions for planning. They all solve the classical problem types. Mes concretament, exploren l'espai d'estats del problema a partir d'un estat inicial fins a trobar l'objectiu designat. 2.4. THE PROBLEM OF ANTICIPATION INTEGRATION UNDER PLANNING 21 Forward Chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence) and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. D'alta banda, aquest metode tendeix a explorar totes les opcions, fins i tot aquelles que no son pertinents. Aixo el fa poc eficaç quan l'espai a explorar es enorme. Backward Chaining is an inference method that can be described (in lay terms) as working backward from the goal(s). It is used in automated theorem provers, proof assistants and other artificial intelligence applications, but it has also been observed in primates. Heuristics under Planning Forward and backward chaining no son eficaços sense una bona funcio heuristica, el qual es dedica a evaluar la distancia entre un estat s i l'objectiu final. Pero per definir-lo es necessari que un individu huma el dissenyi i l'adapti al problema en questio. Partial-Order Planning Forward and Backward State-Space Search Solution path imposes total ordering on actions in a plan. e.g. do X then Y then Z ... (a sequence of actions). But with Partial-Order Planning, solutions define multiple action orderings (partial ordering: a set of sequences achieving goals). Allows agent to use alternate concrete plan if an action becomes unavailable. Tackle subgoals independently, merge resulting plans achieved by searching in a space of partially-ordered plans, rather than states of the environment. 2.4 The Problem of Anticipation Integration under Planning Anticipation it's already itself a big challenge. Even whether it's basic. But in addition, to integrate with planning it's even worse given the SACECA. The next points explain different problem which the project could face while working on it: • Small testing area: SACECA doesn't have a lot of objects to be tested. There are gauges but not with different options. Apparently, it can difficult to demonstrate that works for all cases. • Modifications on SACECA project: can be necessary to change SACECA skeleton to add anticipation. Which could lead to a difficult implementation. Chapter 3 SACECA Platform This chapter describes which SACECA parameters are necessary to know. Because they'll conditionate planning and anticipation. Avatar this concept does reference to SACECA inhabitants. They represent a person. Goal defines which objective must satisfy the avatar. So, planning algorithm must obey this Goal. Gauge avatars have several gauges to satisfy, otherwise they would die. At the time of the project there are four: Hunger, Tiredness, Happiness and Thirsty. It's an integer number which ranges from 0 to 100. In addition, it has a threshold that when Gauge value underpass it, a Goal is created to satisfy Gauge. It decreases depending on each Avatar. It's easy to know how it works. In a nutshell, every X clock "ticks" the value decreases by one unit. Services every object found in SACECA can offer a service. Those services let an avatar to satisfy their needs directly or indirectly-intermediate steps-. Some services have prerequisites that they must be accomplished before execution. For example: have enough money to buy a can. Properties avatar have properties which define them. Those values are dynamic. They have a name, a type and a default value. DefaultPlanningModule.java this file contains the first planning algorithm used which used SACECA in the first version. In the next part, It's explained carefully. CHAPTER 4. PLANNING UNDER SACECA 4.1.5 Related functions The purpose of this part is to describe what the most important functions do. As the idea is to help to improve the comprehension of the DFS algorithm in SACECA. Goal: seemsReached(JexlContent) This function evaluates the agents goals with the help of Jexl library. Those goals are defined by object service conditions. if (!goal.seemsReached(new MemoryAwareU3duJexlContext(agent))) Service: isUsable(object, clonedAgentMemory, null) As it's name says, according to agent (important) memory, it tries to know if it's possible to use the desired object. This means the agent satisfies the preconditions imposed by the service. For example, it has enough money to buy a can. handleWalkTo(object, clonedAgent, service.getMaxDistanceForUsage()) Imagine that given an object, an agent and a maximum distance we try to figure out if the place is reachable. As it has been said before, the object could be surrounded by an other. Which it doesn't let you use it. Finally, it returns a PlanElement or null. service: execute(object, clonedAgentMemory, null, ExecutionMode.VIRTUAL) Giving the object and the agents memory it applies the effects of a service on the agent. For example, if the agents goes to a restaurant, the amount of money have to be reduced and his hunger bar set to maximum. The Virtual execution mode only modifies the agents memory. In normal way, the agent would execute it in real life. 4.1.6 Detailed Example A complete example is provided to give a better comprehension. So, the next chapter is a complete explanation of what planning involves in SACECA. Therefore, to provide an example it's necessary to define which properties and data have the agent, as well as what kind of actions can be performed. 4.1.6.1 Initial Data Those properties can be found in SACECA application, in Memory tab. After that, it requires to search Me field. Agent properties defined by SACECA are shown there.
HAL (Le Centre pour la Communication Scientifique Directe), 2006
G.-L. Baron, J. Baudé & B. de La …, 1993
We study in this paper the impact of Word Sense Disambiguation (WSD) on Query Expansion (QE) for ... more We study in this paper the impact of Word Sense Disambiguation (WSD) on Query Expansion (QE) for monolingual intelligent information retrieval. The proposed approaches for WSD and QE are based on corpus analysis using co-occurrence graphs modelled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantages of a double measure (possibility and necessity). Our experiments are performed using the standard ROMANSEVAL test collection for the WSD task and the CLEF-2003 benchmark for the QE process in French monolingual Information Retrieval (IR) evaluation. The results show the positive impact of WSD on QE based on the recall/precision standard metrics.
International Conference on Information and Knowledge Engineering, 2007
Proc. 9th Symposium on Languages for special Purposes. Bergen (Norway), 1993
The Intelligent Dictionary Help System (IDHS) is a monolingual (explanatory) dictionary system.[1... more The Intelligent Dictionary Help System (IDHS) is a monolingual (explanatory) dictionary system.[1–4] Its design was conceived from the study of questions that human users would like to have answered when consulting a dictionary. The fact that it is intended for people instead of automatic processing distinguishes it from other systems dealing with the acquisition of semantic knowledge from conventional dictionaries. The system provides various access possibilities to the data, allowing the deduction of implicit knowledge from ...
This paper presents a web information retrieval system based on Hierarchical Small-Worlds (HSW) a... more This paper presents a web information retrieval system based on Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). The first HSW consists in structuring the "Google" search results in dense zones of web pages which strongly depend on each other. We thus reveal dense clouds of pages which "speak" more or less about the same subject and which all strongly answer the user’s query. The goal of the second HSW consists in considering the query as multiple in the sense that we don’t seek only the keyword in the web pages but also its synonyms. The PN generates the mixing of these two HSW in order to organize the searched documents according to user’s preferences. Indeed, SARIPOD is a new approach for Information Retrieval Model based on possibility and necessity measures. This model encodes relationship dependencies existing between query terms and web documents through naïve possibilistic networks and quantifies these relationships by two measures: possib...
Procesamiento Del Lenguaje Natural, 1991
DIAC, diccionario inteligente de ayuda a la comprensión, se ha concebido como un sistema computac... more DIAC, diccionario inteligente de ayuda a la comprensión, se ha concebido como un sistema computacional para facilitar y enriquecer la utilización por parte de un usuario humano de la información contenida en un diccionario monolingüe. El propio sistema está dotado de capacidad de deducción, de forma que pueda responder a solicitudes no triviales del usuario. DIAC se está desarrollando a partir de una Base de Datos Diccionarial (BDD), cuyo contenido se ha analizado para estructurar una Base de Conocimientos (BCD), mediante la especificación de la semántica asociada a las diversas estructuras definitorias. Esta BCD es el soporte sobre el que estamos desarrollando el sistema de explotación "inteligente". Para ello es preciso determinar y especificar cuáles son las necesidades del usuario a las que habrá que responder. Desde este punto de vista la BCD se concibe como una estructura de Tipos Abstractos de Datos caracterizada por un conjunto de operaciones. En el presente artícu...
Morphological disambiguation of Arabic words consists in identifying their appropriate morphologi... more Morphological disambiguation of Arabic words consists in identifying their appropriate morphological analysis. In this paper, we present three models of morphological disambiguation of non-vocalized Arabic texts based on possibilistic classification. This approach deals with imprecise training and testing datasets, as we learn from untagged texts. We experiment our approach on two corpora i.e. the Hadith corpus and the Arabic Treebank. These corpora contain data of different types: traditional and modern. We compare our models to probabilistic and statistical classifiers. To do this, we transform the structure of the training and the test sets to deal with imprecise data. Mots-clés : Traitement Automatique des Langues Naturelles, Désambiguïsation Morphologique de l’Arabe, Théorie des Possibilités, Classification Possibiliste.
This paper presents an Internet information retrieval system based on Hierarchical Small-Worlds (... more This paper presents an Internet information retrieval system based on Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). The first HSW, for the words of the French language, is used to take account of the dependences between these words. The second HSW is devoted to the web pages required and translated in the same way the dependences between these pages. The PN generates the mixing of these two HSW in order to organize the sershed documents according to the user profile. Our system propose a mixed approach for Information Retrieval Model based on possibility and necessity measures. This model encodes relationship dependencies existing between query terms and web documents through naïve possibilistic networks and quantifies these relationships by two measures: possibility and necessity. The retrieved documents are those which are necessarily or possibly relevant given a user's query. The search process restores the plausibly or necessarily relevant documents for a ...
This paper presents and experiments a new approach for automatic word sense disambiguation (WSD) ... more This paper presents and experiments a new approach for automatic word sense disambiguation (WSD) applied for French texts. First, we are inspired from possibility theory by taking advantage of a double relevance measure (possibility and necessity) between words and their contexts. Second, we propose, analyze and compare two different training methods: judgment and dictionary based training. Third, we summarize and discuss the overall performance of the various performed tests in a global analysis way. In order to assess and compare our approach with similar WSD systems we performed experiments on the standard ROMANSEVAL test collection.
International Journal on Knowledge and Information Systems. 01/2014;
Applications of Artificial Intelligence in Engineering VI, 1991
Knowledge and Information Systems, 2014
This paper proposes and assesses a new possibilistic approach for automatic monolingual word sens... more This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems.
Knowledge and Information Systems, 2014
This paper proposes and assesses a new possibilistic approach for automatic monolingual word sens... more This paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems.