Célia da Costa Pereira

Multidimensional relevance: Prioritized aggregation in a personalized Information Retrieval setting

A new model for aggregating multiple criteria evaluations for relevance assessment is proposed. An Information Retrieval context is considered, where relevance is modeled as a multidimensional property of documents. The usefulness and effectiveness of such a model are demonstrated by means of a case study on personalized Information Retrieval with multi-criteria relevance. The following criteria are considered to estimate document relevance: aboutness, coverage, appropriateness, and reliability. The originality of this approach lies in the aggregation of the considered criteria in a prioritized way, by considering the existence of a prioritization relationship over the criteria. Such a prioritization is modeled by making the weights associated to a criterion dependent upon the satisfaction of the higher-priority criteria. This way, it is possible to take into account the fact that the weight of a less important criterion should be proportional to the satisfaction degree of the more important criterion. Experimental evaluations are also reported.

Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis

AbstractAn emerging field within sentiment analysis concerns the investigation about how sentiment polarities associated with concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset. The results demonstrate its viability in real-world cases.

Multidimensional Relevance: A New Aggregation Criterion

In this paper, a new model for aggregating multiple criteria evaluations for relevance assessment is proposed. An information retrieval context is considered, where relevance is modelled as a multidimensional property of documents. In the paper, the proposed aggregation operator is applied to define a model for personalized Information Retrieval (IR), in which four criteria are considered in order to assess document relevance: aboutness , coverage , appropriateness and reliability . The originality of this approach lies in the aggregation of the considered criteria in a prioritized way, by considering the existence of a prioritization relationship over the criteria. Such a prioritization is modeled by making the weights associated with a criterion dependent upon the satisfaction of the higher-priority criteria. This way, it is possible to take into account the fact that the weight of a less important criterion should be proportional to the satisfaction degree of the more important criterion. In the paper, some preliminary experimental results are also reported.

A conceptual representation of documents and queries for information retrieval systems by using light ontologies

This article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-Hoc collections demonstrate the effectiveness of the proposed approach.

Changing one's mind: erase or rewind? possibilistic belief revision with fuzzy argumentation based on trust

We address the issue, in cognitive agents, of possible loss of previous information, which later might turn out to be correct when new information becomes available. To this aim, we propose a framework for changing the agents mind without erasing forever previous information, thus allowing its recovery in case the change turns out to be wrong. In this new framework, a piece of information is represented as an argument which can be more or less accepted depending on the trustworthiness of the agent who proposes it. We adopt possibility theory to represent uncertainty about the information, and to model the fact that information sources can be only partially trusted. The originality of the proposed framework lies in the following two points: (i) argument reinstatement is mirrored in belief reinstatement in order to avoid the loss of previous information; (ii) new incoming information is represented under the form of arguments and it is associated with a plausibility degree depending on the trustworthiness of the information source.

A Fuzzy System for Concept-Level Sentiment Analysis

An emerging field within Sentiment Analysis concerns the investigation about how sentiment concepts have to be adapted with respect to the different domains in which they are used. In the context of the Concept-Level Sentiment Analysis Challenge, we presented a system whose aims are twofold: (i) the implementation of a learning approach able to model fuzzy functions used for building the relationships graph representing the appropriateness between sentiment concepts and different domains (Task 1); and (ii) the development of a semantic resource based on the connection between an extended version of WordNet, SenticNet, and ConceptNet, that has been used both for extracting concepts (Task 2) and for classifying sentences within specific domains (Task 3).

Planning with graded nondeterministic actions : A possibilistic approach

This article proposes a framework for planning under uncertainty given a partially known initial state and a set of actions having nondeterministic (disjunctive) effects, some being more possible (normal) than the others. The problem, henceforth called possibilistic planning problem, is represented in an extension of the STRIPS formalism in which the initial state of the world and the graded nondeterministic effects of actions are described by possibility distributions. Two notions of solution plans are introduced: γ‐acceptable plans that lead to a goal state with a certainty greater than a given threshold γ, and optimally safe plans that lead to a goal state with maximal certainty. It is shown that the search of a γ‐acceptable plan amounts to solve a derived planning problem that has only pure (nongraded) nondeterministic actions. A sound and complete partial order planning algorithm, called NDP, has been developed for such classical nondeterministic planning problems. The generation of γ‐acceptable and optimally safe plans is achieved by two sound and complete planning algorithms: POSPLAN that relies on NDP, and POSPLAN* that can be seen as a hierarchical version of POSPLAN. The possibilistic planning framework is illustrated throughout the article by an example in the agronomic domain.

Norms in MAS: Definitions and Related Concepts

In this chapter we provide an introductory presentation of normative multi-agent systems (nMAS). The key idea of the chapter is that any definition of nMAS should preliminarily clarify meaning, scope, and function of the concept of norm. On account of this idea, we focus on three definitions and some related requirements for nMAS. For each of such definitions we propose some guidelines for developing nMAS. Second, we suggest how to relate the concept of nMAS to different conceptions of norms and how norms can be used within the systems. Finally, we identify some specific issues that open research questions or that exhibit interesting overlaps with other disciplines.

An Evolutionary Approach to Multiperiod Asset Allocation

Portfolio construction can become a very complicated problem, as regulatory constraints, individual investor’s requirements, non-trivial indices of risk and subjective quality measures are taken into account, together with multiple investment horizons and cash-flow planning. This problem is approached using a tree of possible scenarios for the future, and an evolutionary algorithm is used to optimize an investment plan against the desired criteria and the possible scenarios. An application to a real defined benefit pension fund case is discussed.

An ontological representation of documents and queries for information retrieval systems

This paper presents a vector space model approach, for representing documents and queries, using concepts instead of terms and WordNet as a light ontology. This way, information overlap is reduced with respect to the classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark showed the effectiveness of the approach.