Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2). 29 November 2012, Lisbon, Portugal

Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2), held in Lisbon, Portugal on 29 November 2012

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

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Fine-grained Arabic named entity recognition

This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly in infection process. Instead of classifying the detected NE phrases into small sets of classes, we target a broader range (i.e. 50 fine-grained classes 'hierarchal-based of two levels') to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in the number of semantic classes and the decrease in semantic differences between fine-grained classes. Our approach to developing fine-grained NER relies on two different supervised Machine Learning (ML) technologies (i.e. Maximum Entropy 'ME' and Conditional Random Fields 'CRF'), which require annotated training data in order to learn by extracting informative features. We develop a methodology which exploit the richness of Arabic Wikipedia (A W) in order to create a scalable fine-grained lexical resource and a corpus automatically. Moreover, two gold-standard created corpora from different genres were also developed to perform comparable evaluation. The thesis also developed a new approach to feature representation by relying on the dependency structure of the sentence to overcome the limitation of traditional window-based (i.e. n-gram) representation. Furthermore, by exploiting the richness of unannotated textual data to extract global informative features using word-level clustering technique was also achieved. Each contribution was evaluated via controlled experiment and reported using three commonly applied metrics, i.e. precision, recall and harmonic F-measure

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal