The Detection of Contradictory Claims in Biomedical Abstracts

Research claims in the biomedical domain are not always consistent, and may even be contradictory. This thesis explores contradictions between research claims in order to determine whether or not it is possible to develop a solution to automate the detection of such phenomena. Such a solution will help decision-makers, including researchers, to alleviate the effects of contradictory claims on their decisions. This study develops two methodologies to construct corpora of contradictions. The first methodology utilises systematic reviews to construct a manually-annotated corpus of contradictions. The second methodology uses a different approach to construct a corpus of contradictions which does not rely on human annotation. This methodology is proposed to overcome the limitations of the manual annotation approach. Moreover, this thesis proposes a pipeline to detect contradictions in abstracts. The pipeline takes a question and a list of research abstracts which may contain answers to it. The output of the pipeline is a list of sentences extracted from abstracts which answer the question, where each sentence is annotated with an assertion value with respect to the question. Claims which feature opposing assertion values are considered as potentially contradictory claims. The research demonstrates that automating the detection of contradictory claims in research abstracts is a feasible problem

Graded Decompositional Semantic Prediction

Compared to traditional approaches, decompositional semantic labeling (DSL) is compelling but introduces complexities for data collection, quality assessment, and modeling. To shed light on these issues and lower barriers to the adoption of DSL or related approaches I bring existing models and novel variations into a shared, familiar framework, facilitating empirical investigation

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Improving Neural Question Answering with Retrieval and Generation

Text-based Question Answering (QA) is a subject of interest both for its practical applications, and as a test-bed to measure the key Artificial Intelligence competencies of Natural Language Processing (NLP) and the representation and application of knowledge. QA has progressed a great deal in recent years by adopting neural networks, the construction of large training datasets, and unsupervised pretraining. Despite these successes, QA models require large amounts of hand-annotated data, struggle to apply supplied knowledge effectively, and can be computationally ex- pensive to operate. In this thesis, we employ natural language generation and information retrieval techniques in order to explore and address these three issues. We first approach the task of Reading Comprehension (RC), with the aim of lifting the requirement for in-domain hand-annotated training data. We describe a method for inducing RC capabilities without requiring hand-annotated RC instances, and demonstrate performance on par with early supervised approaches. We then explore multi-lingual RC, and develop a dataset to evaluate methods which enable training RC models in one language, and testing them in another. Second, we explore open-domain QA (ODQA), and consider how to build mod- els which best leverage the knowledge contained in a Wikipedia text corpus. We demonstrate that retrieval-augmentation greatly improves the factual predictions of large pretrained language models in unsupervised settings. We then introduce a class of retrieval-augmented generator model, and demonstrate its strength and flexibility across a range of knowledge-intensive NLP tasks, including ODQA. Lastly, we study the relationship between memorisation and generalisation in ODQA, developing a behavioural framework based on memorisation to contextualise the performance of ODQA models. Based on these insights, we introduce a class of ODQA model based on the concept of representing knowledge as question- answer pairs, and demonstrate how, by using question generation, such models can achieve high accuracy, fast inference, and well-calibrated predictions

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

Complex question answering : minimizing the gaps and beyond

xi, 192 leaves : ill. ; 29 cmCurrent Question Answering (QA) systems have been significantly advanced in demonstrating finer abilities to answer simple factoid and list questions. Such questions are easier to process as they require small snippets of texts as the answers. However, there is a category of questions that represents a more complex information need, which cannot be satisfied easily by simply extracting a single entity or a single sentence. For example, the question: “How was Japan affected by the earthquake?” suggests that the inquirer is looking for information in the context of a wider perspective. We call these “complex questions” and focus on the task of answering them with the intention to minimize the existing gaps in the literature. The major limitation of the available search and QA systems is that they lack a way of measuring whether a user is satisfied with the information provided. This was our motivation to propose a reinforcement learning formulation to the complex question answering problem. Next, we presented an integer linear programming formulation where sentence compression models were applied for the query-focused multi-document summarization task in order to investigate if sentence compression improves the overall performance. Both compression and summarization were considered as global optimization problems. We also investigated the impact of syntactic and semantic information in a graph-based random walk method for answering complex questions. Decomposing a complex question into a series of simple questions and then reusing the techniques developed for answering simple questions is an effective means of answering complex questions. We proposed a supervised approach for automatically learning good decompositions of complex questions in this work. A complex question often asks about a topic of user’s interest. Therefore, the problem of complex question decomposition closely relates to the problem of topic to question generation. We addressed this challenge and proposed a topic to question generation approach to enhance the scope of our problem domain