María Lucila Morales-Rodríguez

Emotional conversational agents in clinical psychology and psychiatry

This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable dialogue, this extension will allow to create a more realistic scenario for the student to diagnose the condition simulated by the conversational agent. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied.

Modeling Facial Expression of Intelligent Virtual Agents

An intelligent virtual agent is an intelligent agent with a digital representation and endowed with conversational capabilities; this interactive character exhibits human-like behavior and communicates with humans or virtual interlocutors using verbal and nonverbal modalities such as gesture and speech. Building credible characters requires a multidisciplinary approach, the behaviors that must be reproduced are very complex and must be decoded by their interlocutors. In this paper we introduce a kinesics model in order to select the nonverbal expression of intelligent virtual agents able to express their emotions and purposes using gestures and face expressions. Our model select the nonverbal expressions based on causal analysis, and data mining algorithms applied to perceptual studies in order to identify significant attributes in emotional face expressions.

Interdependent Projects Selection with Preference Incorporation

The Project Portfolio Problem (PPP) has been solved through different approaches. The success of some of them is related to a proper application of the decision-maker’s preferences, and a correct identification of organization’s resource practices and conditions. However, there are still a small number of classes of PPP that have been solved using these approaches, and there is also a need for increasing them. Due to this situation, the present research develops a strategy, based on ant colony optimization that incorporates the decision-maker’s preferences into the solution of a case of PPP under conditions of synergy, cannibalization, redundancy, and with interactions between projects. The algorithm was experimentally tested, and the results show a good performance of it over a random set of instances.

A Grey Mathematics Approach for Evolutionary Multi-objective Metaheuristic of Project Portfolio Selection

The aim of this chapter is to present the results of the comparison between the solutions obtained with the grey mathematics and the solutions obtained without the grey mathematics. The grey mathematics is used to represent the uncertainty associate with real-life decision-making. We define a multi-objective algorithm to perform the comparison between algorithms. The results obtained show that the approach using grey mathematics outperforms the results without grey mathematics.

A Dialogue Interaction Module for a Decision Support System Based on Argumentation Schemes to Public Project Portfolio

Organizations are facing the problem of having more projects than resources to implement them. In this paper, we present a dialogue interaction module of a framework for a Decision Support System (DSS) to aid in the selection of public project portfolios. The Interaction module of this DSS is based on multiple argumentation schemes and dialogue games that not only allow the system to generate and justify a recommendation. This module is also able to obtain new information during the dialogue that allows changing the recommendation according to the Decision Maker’s preferences. Researchers have commonly addressed the public portfolio selection problem with multicriteria algorithms. However, in the real life the final selection of the solution depends on the decision maker (DM). We modeled the reasoning of DM by a Dialogue Corpus. This corpus is a database, supported by an argument tree that validates the system’s recommendations with the preferences of the DM.

Analysis of Some Database Schemas Used to Evaluate Natural Language Interfaces to Databases

Most research work about the development of Natural Language Interface to Databases (NLIDB) has been focused on the study of the interpretation and translation from natural language queries to SQL queries. For this purpose and in order to improve the performance in a NLIDB, researchers have addressed different issues related to natural language processing. In addition to this, we consider that the performance of a NLIDB also depends on its ability to adapt to a database schema. For this reason, we analyzed the Geobase, ATIS and Northwind database schemas, commonly used to evaluate NLIDBs. As a result of this analysis, we present in this paper some issues arising from the three database schemas analyzed, which they should be considered in the implementation of a NLIDB to improve its performance.

Using Semantic Representations to Facilitate the Domain-Knowledge Portability of a Natural Language Interface to Databases

Our research is focused on the implementation of a Natural Language Interface to Database. We propose the use of ontologies to model the knowledge required by the interface with the aim of correctly answering natural language queries and facilitate its configuration on other databases. The knowledge of our interface is composed by modeling information about the database schema, its relationship to natural language and some linguistic functions. The design of this modeling allows users to configure the interface without performing complex and tedious tasks, facilitating its portability to other databases. To evaluate the knowledge-domain portability, we configured our interface and the commercial interface ELF in the Northwind database. The results obtained of the experimentation show that the knowledge modeled in our interface allowed it to achieve a good performance.

Automatic Extraction of Hyponymy-Hypernymy Lexical Relations between Nouns from a Spanish Dictionary

In this paper a method is presented which permits to automatically extract lexical-semantic relations between nouns (specifically for concrete nouns since they have a well structured taxonomy). From the definitions of the entries in a Spanish dictionary, the hypernym of an entry is extracted from the entry definition according to the basic assumption that the first noun in the definition is the entry hypernym. After obtaining the hypernym for each entry, multilayered hyponymy-hyperonymy relations are generated from a noun, which is considered the root of the domain. The domains for which this approach was tested were zoology and botany. Five levels of hyponymy-hypernymy relations were generated for each domain. For the zoology domain a total of 1,326 relations was obtained with an average percentage of correctly generated relations (precision) of 84.31% for the five levels. 91.32% of all the relations of this domain were obtained in the first three levels, and for each of these levels the precision exceeds 96%. For the botany domain a total of 1,199 relations was obtained, with an average precision of 71.31% for the five levels. 90.76% of all the relations of this domain were obtained in the first level, and for this level the precision exceeds 99%.

Semi-automatic Generation of Subcategorization Frames for Spanish Verbs Using Ontologies and Verbs Functional Class

This work deals with the semi-automatic generation of subcategorization frames (SCFs) of Spanish verbs; specifically, given a set of verbs in Spanish and their respective sense, their SCFs are obtained. The acquisition of SCFs in Spanish has been approached in different works: in some the frames are generated manually, while in others they are obtained semi-automatically from a tagged corpus; unfortunately in this case, the results depend on the characteristics of the texts used. The method proposed in this document combines an ontology-based approach (through lexical relations of verbs) and linguistic knowledge (functional class of verbs). The relations among base verbs and other verbs were obtained from the Spanish WordNet ontology, which contains lexical relations among words. Also, the existing relation between the SCF and the functional class of verbs was used to generate the SCFs. In order to evaluate the method, the SCFs for 44 base verbs were generated manually, from which 239 SCFs were automatically generated and validated, yielding an accuracy of 89.38%.

Ontology-Based Approach for Semi-automatic Generation of Subcategorization Frames for Spanish Verbs

This work deals with the semi-automatic generation of subcategorization frames (SCFs) of Spanish verbs; specifically, given a set of verbs in Spanish and their respective sense, their SCFs are obtained. The acquisition of SCFs in Spanish has been approached in different works: in some the frames are generated manually, while in others they are obtained semi-automatically from a tagged corpus; unfortunately in this case, the results depend on the characteristics of the texts used. The method proposed in this document combines an ontology-based approach (through lexical relations of verbs) and linguistic knowledge (functional class of verbs). The relations among base verbs and other verbs were obtained from the Spanish WordNet ontology, which contains lexical relations among words. Also, the existing relation between the SCF and the functional class of verbs was used to generate the SCFs. In order to evaluate the method the SCFs for 44 base verbs were generated manually, from which 239 SCFs were semi-automatically generated and validated, yielding an accuracy of 89.38%.