Automatic Generation of Titles for a Corpus of Questions

This paper describes the followed methodology to automatically generate titles for a corpus of questions that belong to sociological opinion polls. Titles for questions have a twofold function: (1) they are the input of user searches and (2) they inform about the whole contents of the question and possible answer options. Thus, generation of titles can be considered as a case of automatic summarization. However, the fact that summarization had to be performed over very short texts together with the aforementioned quality conditions imposed on new generated titles led the authors to follow knowledge-rich and domain-dependent strategies for summarization, disregarding the more frequent extractive techniques for summarization

Multilingual manager: a new strategic role in organizations

Today?s knowledge management (KM) systems seldom account for language management and, especially, multilingual information processing. Document management is one of the strongest components of KM systems. If these systems do not include a multilingual knowledge management policy, intranet searches, excessive document space occupancy and redundant information slow down what are the most effective processes in a single language environment. In this paper, we model information flow from the sources of knowledge to the persons/systems searching for specific information. Within this framework, we focus on the importance of multilingual information processing, which is a hugely complex component of modern organizations

Lack of answer estimation by fuzzy control

The problem of the lack of answer in questions of survey is usually dealt with different estimation and classification procedures from the answers to other questions. In this document, the results of applying fuzzy control methods for the vote -one of the variables with bigger lack of answer in opinion polls- are presented

Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs

There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data

Uso de un modelo de aprendizaje para un sistema complejo de diagnostico industrial con limitación temporal.

Tras una introducción a la necesidad de estudio en este tema, se muestran las distintas ramas que los investigadores van siguiendo en la actualidad, exponiéndose las diferencias entre el diagnóstico médico y el industrial, así como la necesidad de estructurar el conocimiento del problema diagnóstico. La aproximación a tiempo real como objetivo y la definición de los sistemas complejos caracterizan el problema propuesto» como método de resolución del problema del diagnóstico» bajo condiciones de tiempo limitado en la respuesta. Como resolución a este problema, se proponen una serie de procedimientos integrados que permiten dar una respuesta según el tiempo disponible y que se resumen en: - Procedimiento de construcción de un árbol de fallos a partir del conocimiento en forma de reglas. - Procedimientos de depuración estructural del árbol de fallos. - Nuevo procedimiento de construcción del conjunto de Conjuntos Mínimos» puerta a puerta del árbol. - Método de resolución de íncertidumbre en los Conjuntos Mínimos en base a parámetros de fiabilidad para el caso de tiempo suficiente. - Definición del concepto de Conjunto Virtual como procedimiento de resolución de tipo estructural del problema del diagnóstico. - Método de resolución de incertidumbre en Conjuntos Virtuales basado en parámetros de fiabilidad. Los métodos propuestos permiten, desde la detección de inconsistencias en el conocimiento, hasta la posibilidad de diagnóstico incompleto, pero seguro, cuando el tiempo es insuficiente, como caracterización del problema del diagnóstico en emergencias

Titulación automática de preguntas en encuestas electorales

This paper describes the work carried out for automatically generating titles for questions included in the opinion polls contained in CIS databases (Centro de Investigaciones Sociológicas – Spanish Center of Sociological Research). In the context of CIS, the title of a question should meet two requirements: from the point of view of form, it has to be grammatically correct and similar in style to existing ones; from the point of view of content, it must contain the subject of the question and the different options for answering. These conditions for form and content of titles discourage the use of techniques used in similar problems, such as automatic abstracting or machine learning with a training corpus, but rather favor a methodology based on an analysis and knowledge of the domain. To illustrate the analysis and the resolution strategy of the problem, we have selected a set of questions related to elections, due to their strategic importance and to CIS’s own specialization in opinion polls. The process followed and the subsequent evaluation of results are discussed in detail, with an assessment of both qualitative and quantitative aspects. The evaluation shows that 88.73% of the generated titles are in strict accordance with CIS’s requisites on form and content, resulting in reduced time spent by the institution’s qualified personnel on manual work.Este artículo describe el trabajo realizado para la generación automática de los títulos de las preguntas pertenecientes a las encuestas de opinión que existen en las bases de datos del CIS (Centro de Investigaciones Sociológicas). Dentro del contexto del CIS, el título de una pregunta debe cumplir dos requisitos: desde el punto de vista de la forma, debe ser gramaticalmente correcto y tener un estilo similar a los ya existentes; y, desde el punto de vista del contenido, debe albergar el tema de la pregunta y las distintas categorías de respuesta. Estas restricciones en cuanto a la forma y al contenido de los títulos desaconsejan el uso de técnicas empleadas en problemas similares, como el resumen automático o aprendizaje automático con corpus de entrenamiento, a favor de una metodología basada en el análisis y conocimiento del dominio. Para ilustrar el análisis y la estrategia de resolución del problema seguidos, hemos seleccionado las preguntas relacionadas con temas electorales, debido a la importancia estratégica y a la especialización del CIS en este tipo de encuestas. Se describe en detalle el procedimiento seguido y la evaluación de los resultados, valorando tanto los aspectos cualitativos como los cuantitativos. La evaluación muestra que el 88,73% de los títulos generados cumplen estrictamente con los requisitos de forma y contenido impuestos por el CIS, lo que supone un ahorro en el trabajo manual del personal cualificado de la institución

TESAURVAI: Extraction, Annotation and Term Organization Tool

Each concrete field of disciplinary or thematic specializations makes use of its own terminology. The compilation, definition, and organization of terms used in a given domain are a basic task, because it becomes the base for the constitution of specialized terminology resources of great usefulness. Thesauri are a type of terminological resource of increasing relevance at the present time; frequently used in the recovery and localization of information in digital environments. The hierarchic organization of terms in a thesaurus helps to optimize searches both in close information systems and open ones like Internet. TESAURVAI is a tool for the extraction, annotation and organization of specialized terms in concrete domains taken from digitized texts. TESAURVAI is one of the tools developed in the context of the project “Búsqueda documental sobre Patrimonio Cultural basada en recursos léxicos multilingües - Patrilex” (HUM2005-07260/FILO), sponsored by the I+D+I National Plan, National Program of Humanities of Spain, having as one of its objectives the creation of a methodology and the necessary tools for the creation of multilingual lexical resources, allowing to support a multilingual documentary search system. PATRILEX works in the concrete domain of Cultural Heritage, and as its source it uses the texts in the section dedicated to this subject in the Web of the Spanish Ministry of Culture

Consumo de sustancias cannabinoides y patología psicótica

El trabajo presentado trata de evaluar las diferencias del patrón de consumo de cannabis en dos muestras de pacientes ingresados en Unidad de Corta Estancia de Psiquiatría en el año 2001 y 2011 respectivamente. La introducción pone de manifiesto la relevancia del estudio, tanto en cuanto a la frecuencia e importancia de la patología psicótica y consumo de cannabis. El estudio tenía como objetivo evaluar el patrón de consumo entre ambas muestras, hipotetizando una menor frecuencia de consumo de cannabis de acuerdo con la disminución de consumo referida en la bibliografía sobre la población general. El estudio detecta una baja frecuencia de referencia al consumo de cannabis en ambas muestras contrariamente a la percepción sentida de un mayor consumo real en este tipo de pacientes, no obstante, se puede concluir que la frecuencia de consumo de cannabis asociado a patología psicótica que requiere ingreso en Unidades de Corta Estancia de Psiquiatría no ha variado en la década estudiada

Cost-utility of attachment-based compassion therapy (ABCT) and Mindfulness-Based Stress Reduction (MBSR) in the management of depressive, anxious, and adjustment disorders in mental health settings: economic evaluation alongside a randomized controlled trial

Objectives: The main objective of this paper was to examine the cost-utility of attachment-based compassion therapy (ABCT) compared to Mindfulness-Based Stress Reduction (MBSR) and treatment-as-usual (TAU) on patients with depressive and/or anxious disorder, or adjustment disorder with depressive and/or anxious symptomatology in terms of effects on quality-adjusted life years (QALYs) as well as healthcare costs from a public healthcare system perspective. Method: A 6-month randomized controlled trial was conducted. Ninety Spanish patients with mental disorders (depressive, anxious, or adjustment disorders) received 8 weekly group sessions of TAU + ABCT, TAU + MBSR, or TAU alone. Data collection took place at pre- and 6-month follow-up. Cost-utility of the two treatment groups (ABCT vs MBSR vs TAU) was compared by examining treatment outcomes in terms of QALYs (obtained with the EQ-5D-3L) and healthcare costs (data about service use obtained with the Client Service Receipt Inventory). Results: Both MBSR and ABCT were more efficient than TAU alone, although the results did not reach statistical significance. Compared to ABCT, MBSR produced an increase both in terms of costs (€53.69, 95% CI [− 571.27 to 513.14]) and effects (0.004 QALYs, 95% CI [− 0.031 to 0.049]); ICUR = €13,422.50/QALY). Both interventions significantly reduced the number of visits to general practice compared to TAU. Conclusions: This study has contributed to the evidence base of mindfulness- and compassion-based programs and provided promising information about the cost-utility of MBSR for patients with emotional disorders. However, the small sample size and short follow-up period limit the generalizability of the findings. Preregistration: Clinicaltrials.gov; NCT03425487

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes