Report of MIRACLE team for the Ad-Hoc track in CLEF 2007

This paper presents the 2007 MIRACLE’s team approach to the AdHoc Information Retrieval track. The work carried out for this campaign has been reduced to monolingual experiments, in the standard and in the robust tracks. No new approaches have been attempted in this campaign, following the procedures established in our participation in previous campaigns. For this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, Hungarian, and Czech. - Robust monolingual: French, English and Portuguese. There is still some room for improvement around multilingual named entities recognition

MIRACLE Progress in Monolingual Information Retrieval at Ad-Hoc CLEF 2007

This paper presents the 2007 MIRACLE’s team approach to the Ad-Hoc Information Retrieval track. The main work carried out for this campaign has been around monolingual experiments, in the standard and in the robust tracks. The most important contributions have been the general introduction of automatic named-entities extraction and the use of Wikipedia resources. For the2007 campaign, runs were submitted for the following languages and tracks: a) Monolingual: Bulgarian, Hungarian, and Czech. b) Robust monolingual: French, English and Portuguese

MIRACLE’s Ad-Hoc and Geographical IR Approaches for CLEF 2006

This paper presents the 2006 Miracle team’s approaches to the Ad-Hoc and Geographical Information Retrieval tasks. A first set of runs was obtained using a set of basic components. Then, by putting together special combinations of these runs, an extended set was obtained. With respect to previous campaigns some improvements have been introduced in our system: an entity recognition prototype is integrated in our tokenization scheme, and the performance of our indexing and retrieval engine has been improved. For GeoCLEF, we tested retrieving using geo-entity and textual references separately, and then combining them with different approaches

Bootstrapping named entity resources for adaptive question answering systems

Los Sistemas de Búsqueda de Respuestas (SBR) amplían las capacidades de un buscador de información tradicional con la capacidad de encontrar respuestas precisas a las preguntas del usuario. El objetivo principal es facilitar el acceso a la información y disminuir el tiempo y el esfuerzo que el usuario debe emplear para encontrar una información concreta en una lista de documentos relevantes. En esta investigación se han abordado dos trabajos relacionados con los SBR. La primera parte presenta una arquitectura para SBR en castellano basada en la combinación y adaptación de diferentes técnicas de Recuperación y de Extracción de Información. Esta arquitectura está integrada por tres módulos principales que incluyen el análisis de la pregunta, la recuperación de pasajes relevantes y la extracción y selección de respuestas. En ella se ha prestado especial atención al tratamiento de las Entidades Nombradas puesto que, con frecuencia, son el tema de las preguntas o son buenas candidatas como respuestas. La propuesta se ha encarnado en el SBR del grupo MIRACLE que ha sido evaluado de forma independiente durante varias ediciones en la tarea compartida CLEF@QA, parte del foro de evaluación competitiva Cross-Language Evaluation Forum (CLEF). Se describen aquí las participaciones y los resultados obtenidos entre 2004 y 2007. El SBR de MIRACLE ha obtenido resultados moderados en el desempeño de la tarea con tasas de respuestas correctas entre el 20% y el 30%. Entre los resultados obtenidos destacan los de la tarea principal de 2005 y la tarea piloto de Búsqueda de Respuestas en tiempo real de 2006, RealTimeQA. Esta última tarea, además de requerir respuestas correctas incluía el tiempo de respuesta como un factor adicional en la evaluación. Estos resultados respaldan la validez de la arquitectura propuesta como una alternativa viable para los SBR sobre colecciones textuales y también corrobora resultados similares para el inglés y otras lenguas. Por otro lado, el análisis de los resultados a lo largo de las diferentes ediciones de CLEF así como la comparación con otros SBR apunta nuevos problemas y retos. Según nuestra experiencia, los sistemas de QA son más complicados de adaptar a otros dominios y lenguas que los sistemas de Recuperación de Información. Este problema viene heredado del uso de herramientas complejas de análisis de lenguaje como analizadores morfológicos, sintácticos y semánticos. Entre estos últimos se cuentan las herramientas para el Reconocimiento y Clasificación de Entidades Nombradas (NERC en inglés) así como para la Detección y Clasificación de Relaciones (RDC en inglés). Debido a la di cultad de adaptación del SBR a distintos dominios y colecciones, en la segunda parte de esta tesis se investiga una propuesta diferente basada en la adquisición de conocimiento mediante métodos de aprendizaje ligeramente supervisado. El objetivo de esta investigación es adquirir recursos semánticos útiles para las tareas de NERC y RDC usando colecciones de textos no anotados. Además, se trata de eliminar la dependencia de herramientas de análisis lingüístico con el fin de facilitar que las técnicas sean portables a diferentes dominios e idiomas. En primer lugar, se ha realizado un estudio de diferentes algoritmos para NERC y RDC de forma semisupervisada a partir de unos pocos ejemplos (bootstrapping). Este trabajo propone primero una arquitectura común y compara diferentes funciones que se han usado en la evaluación y selección de resultados intermedios, tanto instancias como patrones. La principal propuesta es un nuevo algoritmo que permite la adquisición simultánea e iterativa de instancias y patrones asociados a una relación. Incluye también la posibilidad de adquirir varias relaciones de forma simultánea y mediante el uso de la hipótesis de exclusividad obtener mejores resultados. Como característica distintiva el algoritmo explora la colección de textos con una estrategia basada en indización, que permite adquirir conocimiento de grandes colecciones. La estrategia de selección de candidatos y la evaluación se basan en la construcción de un grafo de instancias y patrones, que justifica nuestro método para la selección de candidatos. Este procedimiento es semejante al frente de exploración de una araña web y permite encontrar las instancias más parecidas a las semillas con las evidencias disponibles. Este algoritmo se ha implementado en el sistema SPINDEL y para su evaluación se ha comenzado con el caso concreto de la adquisición de recursos para las clases de Entidades Nombradas más comunes, Persona, Lugar y Organización. El objetivo es adquirir nombres asociados a cada una de las categorías así como patrones contextuales que permitan detectar menciones asociadas a una clase. Se presentan resultados para la adquisición de dos idiomas distintos, castellano e inglés, y para el castellano, en dos dominios diferentes, noticias y textos de una enciclopedia colaborativa, Wikipedia. En ambos casos el uso de herramientas de análisis lingüístico se ha limitado de acuerdo con el objetivo de avanzar hacia la independencia de idioma. Las listas adquiridas mediante bootstrapping parten de menos de 40 semillas por clase y obtienen del orden de 30.000 instancias de calidad variable. Además se obtienen listas de patrones indicativos asociados a cada clase de entidad. La evaluación indirecta confirma la utilidad de ambos recursos en la clasificación de Entidades Nombradas usando un enfoque simple basado únicamente en diccionarios. La mejor configuración obtiene para la clasificación en castellano una medida F de 67,17 y para inglés de 55,99. Además se confirma la utilidad de los patrones adquiridos que en ambos casos ayudan a mejorar la cobertura. El módulo requiere menor esfuerzo de desarrollo que los enfoques supervisados, si incluimos la necesidad de anotación, aunque su rendimiento es inferior por el momento. En definitiva, esta investigación constituye un primer paso hacia el desarrollo de aplicaciones semánticas como los SBR que requieran menos esfuerzo de adaptación a un dominio o lenguaje nuevo.-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Question Answering (QA) systems add new capabilities to traditional search engines with the ability to find precise answers to user questions. Their objective is to enable easier information access by reducing the time and effort that the user requires to find a concrete information among a list of relevant documents. In this thesis we have carried out two works related with QA systems. The first part introduces an architecture for QA systems for Spanish which is based on the combination and adaptation of different techniques from Information Retrieval (IR) and Information Extraction (IE). This architecture is composed by three modules that include question analysis, relevant passage retrieval and answer extraction and selection. The appropriate processing of Named Entities (NE) has received special attention because of their importance as question themes and candidate answers. The proposed architecture has been implemented as part of the MIRACLE QA system. This system has taken part in independent evaluations like the CLEF@QA track in the Cross-Language Evaluation Forum (CLEF). Results from 2004 to 2007 campaigns as well as the details and the evolution of the system have been described in deep. The MIRACLE QA system has obtained moderate performance with a first answer accuracy ranging between 20% and 30%. Nevertheless, it is important to highlight the results obtained in the 2005 main QA task and the RealTimeQA pilot task in 2006. The last one included response time as an important additional variable of the evaluation. These results back the proposed architecture as an option for QA from textual collection and confirm similar findings obtained for English and other languages. On the other hand, the analysis of the results along evaluation campaigns and the comparison with other QA systems point problems with current systems and new challenges. According to our experience, it is more dificult to tailor QA systems to different domains and languages than IR systems. The problem is inherited by the use of complex language analysis tools like POS taggers, parsers and other semantic analyzers, like NE Recognition and Classification (NERC) and Relation Detection and Characterization (RDC) tools. The second part of this thesis tackles this problem and proposes a different approach to adapting QA systems for di erent languages and collections. The proposal focuses on acquiring knowledge for the semantic analyzers based on lightly supervised approaches. The goal is to obtain useful resources that help to perform NERC or RDC using as few annotated resources as possible. Besides, we try to avoid dependencies from other language analysis tools with the purpose that these methods apply to different languages and domains. First of all, we have study previous work on building NERC and RDC modules with few supervision, particularly bootstrapping methods. We propose a common framework for different bootstrapping systems that help to unify different evaluation functions for intermediate results. The main proposal is a new algorithm that is able to simultaneously acquire instances and patterns associated to a relation of interest. It also uses mutual exclusion among relations to reduce concept drift and achieve better results. A distinctive characteristic is that it uses a query based exploration strategy of the text collection which enables their use for larger collections. Candidate selection and evaluation are based on incrementally building a graph of instances and patterns which also justifies our evaluation function. The discovery approach is analogous to the front of exploration in a web crawler and it is able to find the most similar instances to the available seeds. This algorithm has been implemented in the SPINDEL system. We have selected for evaluation the task of acquiring resources for the most common NE classes, Person, Location and Organization. The objective is to acquire name instances that belong to any of the classes as well as contextual patterns that help to detect mentions of NE that belong to that class. We present results for the acquisition of resources from raw text from two different languages, Spanish and English. We also performed experiments for Spanish in two different collections, news and texts from a collaborative encyclopedia, Wikipedia. Both cases are tackled with limited language analysis tools and resources. With an initial list of 40 instance seeds, the bootstrapping process is able to acquire large name lists containing up to 30.000 instances with a variable quality. Besides, large lists of indicative patterns are obtained too. Our indirect evaluation confirms the utility of both resources to classify NE using a simple dictionary recognition approach. Best results for Spanish obtained a F-score of 67,17 and for English this value is 55,99. The module requires much less development effort than annotation for supervised algorithms although the performance is not in pair yet. This research is a first step towards the development of semantic applications like QA for a new language or domain with no annotated corpora that requires less adaptation effort

An enhanced concept based approach medical information retrieval to address readability, vocabulary and presentation issues

Querying of health information retrieval for health advice has now become a general and notable task performed by individuals on the Internet. However, the failure of the existing approaches to integrate program modules that would address the information needs of all categories of end-users remains. This study focused on proposing an improved framework and designing an enhanced concept based approach (ECBA) for medical information retrieval that would better address readability, vocabulary mismatched and presentation issues by generating medical discharge documents and medical search queries results in both medical expert and layman’s forms. Three special program modules were designed and integrated in the enhanced concept based approach namely: medical terms control module, vocabulary controlled module and readability module to specifically address the information needs of both medical experts and laymen end-users. Eight benched marked datasets namely: Medline, UMLS, MeSH, Metamap, Metathesaurus, Diagnosia 7, Khresmoi Project 6 and Genetic Home Reference were used in validating the systems performance. Additionally, the ECBA was compared using three existing approaches such as concept based approach (CBA), query likelihood model (QLM) and latent semantic indexing (LSI). The evaluation was conducted using the performance and statistical metrics: P@40, NDCG@40, MAP, Analysis of Variance (ANOVA) and Turkey HSD Tests. The outcome of the final experimental results obtained shows that, the ECBA consistently obtained above 93% accuracy rate results on Medline, UMLS and MeSH Datasets, 92% on Metamap, Metathesaurus and Diagnosia 7 datasets and 91% on Khresmoi Project 6 and Genetic Home Reference datasets. Also, the statistical analysis performance results obtained by each of the four approaches: ECBA, CBA, QLM and LSI shows that, there is a significant difference among their Mean Scores, hence, the null hypothesis of no significant difference was rejected

Query refinement for patent prior art search

A patent is a contract between the inventor and the state, granting a limited time period to the inventor to exploit his invention. In exchange, the inventor must put a detailed description of his invention in the public domain. Patents can encourage innovation and economic growth but at the time of economic crisis patents can hamper such growth. The long duration of the application process is a big obstacle that needs to be addressed to maximize the benefit of patents on innovation and economy. This time can be significantly improved by changing the way we search the patent and non-patent literature.Despite the recent advancement of general information retrieval and the revolution of Web Search engines, there is still a huge gap between the emerging technologies from the research labs and adapted by major Internet search engines, and the systems which are in use by the patent search communities.In this thesis we investigate the problem of patent prior art search in patent retrieval with the goal of finding documents which describe the idea of a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Other relevance evidences (e.g. classification tags, and bibliographical data) provide additional details about the underlying information need of the query patent. The first goal of this thesis is to estimate a uni-gram query model from the textual fields of a query patent. We then improve the initial query representation using noun phrases extracted from the query patent. We show that expansion in a query-dependent manner is useful.The second contribution of this thesis is to address the term mismatch problem from a query formulation point of view by integrating multiple relevance evidences associated with the query patent. To do this, we enhance the initial representation of the query with the term distribution of the community of inventors related to the topic of the query patent. We then build a lexicon using classification tags and show that query expansion using this lexicon and considering proximity information (between query and expansion terms) can improve the retrieval performance. We perform an empirical evaluation of our proposed models on two patent datasets. The experimental results show that our proposed models can achieve significantly better results than the baseline and other enhanced models

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

Printing Spinoza

In this descriptive bibliography Jeroen van de Ven examines all editions of Spinoza’s writings published between 1663 and 1694: his exposition of Descartes’s ‘Principles’ with the ‘Metaphysical Thoughts’, the ‘Theological-Political Treatise’, and the posthumous writings, with the ‘Ethics’

Leveraging Semantic Annotations for Event-focused Search & Summarization

Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.Im heutigen Big Data Zeitalters existieren überwältigende Mengen an Textinformationen, die über mehrere Quellen verteilt sind und ein hohes Maß an Redundanz haben. Durch diese Gegebenheiten ist eine Retroperspektive auf vergangene Ereignisse für Konsumenten nur schwer möglich. Eine plausible Lösung ist die Verknüpfung semantisch ähnlicher, aber über mehrere Quellen verteilter Informationen, um dadurch eine Struktur zu erzwingen, die mehrere Zugriffspfade auf relevante Informationen, bietet. Vor diesem Hintergrund benutzt diese Dissertation Wikipedia und Onlinenachrichten als zwei prominente, aber dennoch grundverschiedene Informationsquellen, um die folgenden drei Probleme anzusprechen: • Wir adressieren ein Verknüpfungsproblem, um Wikipedia-Auszüge mit Nachrichtenartikeln zu verbinden und das Problem in eine Information-Retrieval-Aufgabe umzuwandeln. Unser neuartiger Ansatz integriert Zeit- und Geobezüge sowie Entitäten mit Text, um relevante Dokumente, die mit einem gegebenen Auszug verknüpft werden können, zu identifizieren. • Wir befassen uns mit einer unüberwachten Extraktionsmethode zur automatischen Zusammenfassung von Texten aus mehreren Dokumenten um Ereigniszusammenfassungen mit fester Länge zu generieren, was eine effiziente Aufnahme von Informationen aus großen Dokumentenmassen ermöglicht. Unser neuartiger Ansatz schlägt eine ganzzahlige lineare Optimierungslösung vor, die globale Inferenzen über Text, Zeit, Geolokationen und mit Ereignis-verbundenen Entitäten zieht. • Um den zeitlichen Fokus kurzer Ereignisbeschreibungen abzuschätzen, stellen wir einen semi-überwachten Ansatz vor, der die Redundanz innerhalb einer langzeitigen Dokumentensammlung ausnutzt, um genaue probabilistische Zeitmodelle abzuschätzen. Umfangreiche experimentelle Auswertungen zeigen die Wirksamkeit und Tragfähigkeit unserer vorgeschlagenen Ansätze zur Erreichung des größeren Ziels

New approaches to interactive multimedia content retrieval from different sources

Mención Internacional en el título de doctorInteractive Multimodal Information Retrieval systems (IMIR) increase the capabilities of traditional search systems with the ability to retrieve information in different types (modes) and from different sources. The increase in online content while diversifying means of access to information (phones, tablets, smart watches) encourages the growing need for this type of system. In this thesis a formal model for describing interactive multimodal information retrieval systems querying various information retrieval engines has been defined. This model includes formal and widespread definition of each component of an IMIR system, namely: multimodal information organized in collections, multimodal query, different retrieval engines, a source management system (handler), a results management module (fusion) and user interactions. This model has been validated in two stages. The first, in a use case focused on information retrieval on sports. A prototype that implements a subset of the features of the model has been developed: a multimodal collection that is semantically related, three types of multimodal queries (text, audio and text + image), six different retrieval engines (question answering, full-text search, search based on ontologies, OCR in image, object detection in image and audio transcription), a strategy for source selection based on rules defined by experts, a strategy of combining results and recording of user interactions. NDCG (normalized discounted cumulative gain) has been used for comparing the results obtained for each retrieval engine. These results are: 10,1% (Question answering), 80% (full text search) and 26;8% (ontology search). These results are on the order of works of the state of art considering forums like CLEF. When the retrieval engine combination is used, the information retrieval performance increases by a percentage gain of 771,4% with question answering, 7,2% with full text search and 145,5% with Ontology search. The second scenario is focused on a prototype retrieving information from social media in the health domain. A prototype has been developed which is based on the proposed model and integrates health domain social media user-generated information, knowledge bases, query, retrieval engines, sources selection module, results' combination module and GUI. In addition, the documents included in the retrieval system have been previously processed by a process that extracts semantic information in health domain. In addition, several adaptation techniques applied to the retrieval functionality of an IMIR system have been defined by analyzing past interactions using decision trees, neural networks and clusters. After modifying the sources selection strategy (handler), the system has been reevaluated using classification techniques. The same queries and relevance judgments done by users in the sports domain prototype will be used for this evaluation. This evaluation compares the normalized discounted cumulative gain (NDCG) measure obtained with two different approaches: the multimodal system using predefined rules and the same multimodal system once the functionality is adapted by past user interactions. The NDCG has shown an improvement between -2,92% and 2,81% depending on the approaches used. We have considered three features to classify the approaches: (i) the classification algorithm; (ii) the query features; and (iii) the scores for computing the orders of retrieval engines. The best result is obtained using probabilities-based classification algorithm, the retrieval engines ranking generated with Averaged-Position score and the mode, type, length and entities of the query. Its NDCG value is 81,54%.Los Sistemas Interactivos de Recuperación de Información Multimodal (IMIR) incrementan las capacidades de los sistemas tradicionales de búsqueda con la posibilidad de recuperar información de diferentes tipos (modos) y a partir de diferentes fuentes. El incremento del contenido en internet a la vez que la diversificación de los medios de acceso a la información (móviles, tabletas, relojes inteligentes) fomenta la necesidad cada vez mayor de este tipo de sistemas. En esta tesis se ha definido un modelo formal para la descripción de sistemas de recuperación de información multimodal e interactivos que consultan varios motores de recuperación. Este modelo incluye la definición formal y generalizada de cada componente de un sistema IMIR, a saber: información multimodal organizada en colecciones, consulta multimodal, diferentes motores de recuperación, sistema de gestión de fuentes (handler), módulo de gestión de resultados (fusión) y las interacciones de los usuarios. Este modelo se ha validado en dos escenarios. El primero, en un caso de uso focalizado en recuperación de información relativa a deportes. Se ha desarrollado un prototipo que implementa un subconjunto de todas las características del modelo: una colección multimodal que se relaciona semánticamente, tres tipos de consultas multimodal (texto, audio y texto + imagen), seis motores diferentes de recuperación (búsqueda de respuestas, búsqueda de texto completo, búsqueda basada en ontologías, OCR en imagen, detección de objetos en imagen y transcripción de audio), una estrategia de selección de fuentes basada en reglas definidas por expertos, una estrategia de combinación de resultados y el registro de las interacciones. Se utiliza la medida NDCG (normalized discounted cumulative gain) para describir los resultados obtenidos por cada motor de recuperación. Estos resultados son: 10,1% (Question Answering), 80% (Búsqueda a texto completo) y 26,8% (Búsqueda en ontologías). Estos resultados están en el orden de los trabajos del estado de arte considerando foros como CLEF (Cross-Language Evaluation Forum). Cuando se utiliza la combinación de motores de recuperación, el rendimiento de recuperación de información se incrementa en un porcentaje de ganancia de 771,4% con Question Answering, 7,2% con Búsqueda a texto completo y 145,5% con Búsqueda en ontologías. El segundo escenario es un prototipo centrado en recuperación de información de medios sociales en el dominio de salud. Se ha desarrollado un prototipo basado en el modelo propuesto y que integra información del dominio de salud generada por el usuario en medios sociales, bases de conocimiento, consulta, motores de recuperación, módulo de selección de fuentes, módulo de combinación de resultados y la interfaz gráfica de usuario. Además, los documentos incluidos en el sistema de recuperación han sido previamente anotados mediante un proceso de extracción de información semántica del dominio de salud. Además, se han definido técnicas de adaptación de la funcionalidad de recuperación de un sistema IMIR analizando interacciones pasadas mediante árboles de decisión, redes neuronales y agrupaciones. Una vez modificada la estrategia de selección de fuentes (handler), se ha evaluado de nuevo el sistema usando técnicas de clasificación. Las mismas consultas y juicios de relevancia realizadas por los usuarios en el primer prototipo sobre deportes se han utilizado para esta evaluación. La evaluación compara la medida NDCG (normalized discounted cumulative gain) obtenida con dos enfoques diferentes: el sistema multimodal usando reglas predefinidas y el mismo sistema multimodal una vez que la funcionalidad se ha adaptado por las interacciones de usuario. El NDCG ha mostrado una mejoría entre -2,92% y 2,81% en función de los métodos utilizados. Hemos considerado tres características para clasificar los enfoques: (i) el algoritmo de clasificación; (ii) las características de la consulta; y (iii) las puntuaciones para el cálculo del orden de los motores de recuperación. El mejor resultado se obtiene utilizando el algoritmo de clasificación basado en probabilidades, las puntuaciones para los motores de recuperación basados en la media de la posición del primer resultado relevante y el modo, el tipo, la longitud y las entidades de la consulta. Su valor de NDCG es 81,54%.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Ana García Serrano.- Secretario: María Belén Ruiz Mezcua.- Vocal: Davide Buscald