Question answering using document tagging and question classification

viii, 139 leaves ; 29 cm.Question answering (QA) is a relatively new area of research. QA is retriecing answers to questions rather than information retrival systems (search engines), which retrieve documents. This means that question answering systems will possibly be the next generation of search engines. What is left to be done to allow QA to be the next generation of search engines? The answer is higher accuracy, which can be achieved by investigating methods of questions answering. I took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available systems to tage the documents. Our system classifies the questions using manually developed rules. I also investigated different ways which can use both these methods to answer questions and found that our methods had a comparable accuracy to some systems that use deeper processing techniques. This thesis includes investigations into modules of a question answering system and gives insights into how to go about developing a question answering system based on document tagging and question classification. I also evaluated our current system with the questions from the TREC 2004 question answering track

Answer extraction for simple and complex questions

xi, 214 leaves : ill. (some col.) ; 29 cm. --When a user is served with a ranked list of relevant documents by the standard document search engines, his search task is usually not over. He has to go through the entire document contents to find the precise piece of information he was looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain question answering. We have considered both simple and complex questions. Simple questions (i.e. factoid and list) are easier to answer than questions that have complex information needs and require inferencing and synthesizing information from multiple documents. Our question answering system for simple questions is based on question classification and document tagging. Question classification extracts useful information (i.e. answer type) about how to answer the question and document tagging extracts useful information from the documents, which is used in finding the answer to the question. For complex questions, we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. One hill climbing local search strategy is used to fine-tune the feature-weights. We also experimented with two unsupervised machine learning techniques: k-means and Expectation Maximization (EM) algorithms and evaluated their performance. For all these methods, we have shown the effects of different kinds of features

Enhancing factoid question answering using frame semantic-based approaches

FrameNet is used to enhance the performance of semantic QA systems. FrameNet is a linguistic resource that encapsulates Frame Semantics and provides scenario-based generalizations over lexical items that share similar semantic backgrounds.Doctor of Philosoph

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

Question Answering using Syntactic Patterns in a Contextual Search Engine

Question Answering (QA) systems promise to enhance both usability and accuracy when searching for knowledge. This thesis presents a prototype QA system built to leverage the extraction capabilities of a modern, context-aware search platform; Fast ESP. Questions in plain English are transformed to queries which target specific entities in the text that correspond with the identified answer types. A small set of unified patterns is demonstrated as adequate to classify a wide variety of syntactic constructs. For the purpose of verifying the answers, a semantic lexicon is compiled using an automated procedure. The whole solution is based on pattern matching and presents this as a viable alternative to deeper linguistic methods

Toponym Disambiguation in Information Retrieval

In recent years, geography has acquired a great importance in the context of Information Retrieval (IR) and, in general, of the automated processing of information in text. Mobile devices that are able to surf the web and at the same time inform about their position are now a common reality, together with applications that can exploit this data to provide users with locally customised information, such as directions or advertisements. Therefore, it is important to deal properly with the geographic information that is included in electronic texts. The majority of such kind of information is contained as place names, or toponyms. Toponym ambiguity represents an important issue in Geographical Information Retrieval (GIR), due to the fact that queries are geographically constrained. There has been a struggle to nd speci c geographical IR methods that actually outperform traditional IR techniques. Toponym ambiguity may constitute a relevant factor in the inability of current GIR systems to take advantage from geographical knowledge. Recently, some Ph.D. theses have dealt with Toponym Disambiguation (TD) from di erent perspectives, from the development of resources for the evaluation of Toponym Disambiguation (Leidner (2007)) to the use of TD to improve geographical scope resolution (Andogah (2010)). The Ph.D. thesis presented here introduces a TD method based on WordNet and carries out a detailed study of the relationship of Toponym Disambiguation to some IR applications, such as GIR, Question Answering (QA) and Web retrieval. The work presented in this thesis starts with an introduction to the applications in which TD may result useful, together with an analysis of the ambiguity of toponyms in news collections. It could not be possible to study the ambiguity of toponyms without studying the resources that are used as placename repositories; these resources are the equivalent to language dictionaries, which provide the di erent meanings of a given word.Buscaldi, D. (2010). Toponym Disambiguation in Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8912Palanci

Representation and Inference for Open-Domain Question Answering: Strength and Limits of two Italian Semantic Lexicons

La ricerca descritta nella tesi è stata dedicata alla costruzione di un prototipo di sistema di Question Answering per la lingua italiana. Il prototipo è stato utilizzato come ambiente di valutazione dell’utilità dell’informazione codificata in due lessici semantici computazionali, ItalWordNet e SIMPLE-CLIPS. Il fine è quello di metter in evidenza ipunti di forza e ilimiti della rappresentazione dell’informazione proposta dai due lessici

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

Towards Collaborative Session-based Semantic Search

In recent years, the most popular web search engines have excelled in their ability to answer short queries that require clear, localized and personalized answers. When it comes to complex exploratory search tasks however, the main challenge for the searcher remains the same as back in the 1990s: Trying to formulate a single query that contains all the right keywords to produce at least some relevant results. In this work we want to investigate new ways to facilitate exploratory search by making use of context information from the user's entire search process. Therefore we present the concept of session-based semantic search, with an optional extension to collaborative search scenarios. To improve the relevance of search results we expand queries with terms from the user's recent query history in the same search context (session-based search). We introduce a novel method for query classification based on statistical topic models which allows us to track the most important topics in a search session so that we can suggest relevant documents that could not be found through keyword matching. To demonstrate the potential of these concepts, we have built the prototype of a session-based semantic search engine which we release as free and open source software. In a qualitative user study that we have conducted, this prototype has shown promising results and was well-received by the participants.:1. Introduction 2. Related Work 2.1. Topic Models 2.1.1. Common Traits 2.1.2. Topic Modeling Techniques 2.1.3. Topic Labeling 2.1.4. Topic Graph Visualization 2.2. Session-based Search 2.3. Query Classification 2.4. Collaborative Search 2.4.1. Aspects of Collaborative Search Systems 2.4.2. Collaborative Information Retrieval Systems 3. Core Concepts 3.1. Session-based Search 3.1.1. Session Data 3.1.2. Query Aggregation 3.2. Topic Centroid 3.2.1. Topic Identification 3.2.2. Topic Shift 3.2.3. Relevance Feedback 3.2.4. Topic Graph Visualization 3.3. Search Strategy 3.3.1. Prerequisites 3.3.2. Search Algorithms 3.3.3. Query Pipeline 3.4. Collaborative Search 3.4.1. Shared Topic Centroid 3.4.2. Group Management 3.4.3. Collaboration 3.5. Discussion 4. Prototype 4.1. Document Collection 4.1.1. Selection Criteria 4.1.2. Data Preparation 4.1.3. Search Index 4.2. Search Engine 4.2.1. Search Algorithms 4.2.2. Query Pipeline 4.2.3. Session Persistence 4.3. User Interface 4.4. Performance Review 4.5. Discussion 5. User Study 5.1. Methods 5.1.1. Procedure 5.1.2. Implementation 5.1.3. Tasks 5.1.4. Questionnaires 5.2. Results 5.2.1. Participants 5.2.2. Task Review 5.2.3. Literature Research Results 5.3. Discussion 6. Conclusion Bibliography Weblinks A. Appendix A.1. Prototype: Source Code A.2. Survey A.2.1. Tasks A.2.2. Document Filter for Google Scholar A.2.3. Questionnaires A.2.4. Participant’s Answers A.2.5. Participant’s Search ResultsDie führenden Web-Suchmaschinen haben sich in den letzten Jahren gegenseitig darin übertroffen, möglichst leicht verständliche, lokalisierte und personalisierte Antworten auf kurze Suchanfragen anzubieten. Bei komplexen explorativen Rechercheaufgaben hingegen ist die größte Herausforderung für den Nutzer immer noch die gleiche wie in den 1990er Jahren: Eine einzige Suchanfrage so zu formulieren, dass alle notwendigen Schlüsselwörter enthalten sind, um zumindest ein paar relevante Ergebnisse zu erhalten. In der vorliegenden Arbeit sollen neue Methoden entwickelt werden, um die explorative Suche zu erleichtern, indem Kontextinformationen aus dem gesamten Suchprozess des Nutzers einbezogen werden. Daher stellen wir das Konzept der sitzungsbasierten semantischen Suche vor, mit einer optionalen Erweiterung auf kollaborative Suchszenarien. Um die Relevanz von Suchergebnissen zu steigern, werden Suchanfragen mit Begriffen aus den letzten Anfragen des Nutzers angereichert, die im selben Suchkontext gestellt wurden (sitzungsbasierte Suche). Außerdem wird ein neuartiger Ansatz zur Klassifizierung von Suchanfragen eingeführt, der auf statistischen Themenmodellen basiert und es uns ermöglicht, die wichtigsten Themen in einer Suchsitzung zu erkennen, um damit weitere relevante Dokumente vorzuschlagen, die nicht durch Keyword-Matching gefunden werden konnten. Um das Potential dieser Konzepte zu demonstrieren, wurde im Rahmen dieser Arbeit der Prototyp einer sitzungsbasierten semantischen Suchmaschine entwickelt, den wir als freie Software veröffentlichen. In einer qualitativen Nutzerstudie hat dieser Prototyp vielversprechende Ergebnisse hervorgebracht und wurde von den Teilnehmern positiv aufgenommen.:1. Introduction 2. Related Work 2.1. Topic Models 2.1.1. Common Traits 2.1.2. Topic Modeling Techniques 2.1.3. Topic Labeling 2.1.4. Topic Graph Visualization 2.2. Session-based Search 2.3. Query Classification 2.4. Collaborative Search 2.4.1. Aspects of Collaborative Search Systems 2.4.2. Collaborative Information Retrieval Systems 3. Core Concepts 3.1. Session-based Search 3.1.1. Session Data 3.1.2. Query Aggregation 3.2. Topic Centroid 3.2.1. Topic Identification 3.2.2. Topic Shift 3.2.3. Relevance Feedback 3.2.4. Topic Graph Visualization 3.3. Search Strategy 3.3.1. Prerequisites 3.3.2. Search Algorithms 3.3.3. Query Pipeline 3.4. Collaborative Search 3.4.1. Shared Topic Centroid 3.4.2. Group Management 3.4.3. Collaboration 3.5. Discussion 4. Prototype 4.1. Document Collection 4.1.1. Selection Criteria 4.1.2. Data Preparation 4.1.3. Search Index 4.2. Search Engine 4.2.1. Search Algorithms 4.2.2. Query Pipeline 4.2.3. Session Persistence 4.3. User Interface 4.4. Performance Review 4.5. Discussion 5. User Study 5.1. Methods 5.1.1. Procedure 5.1.2. Implementation 5.1.3. Tasks 5.1.4. Questionnaires 5.2. Results 5.2.1. Participants 5.2.2. Task Review 5.2.3. Literature Research Results 5.3. Discussion 6. Conclusion Bibliography Weblinks A. Appendix A.1. Prototype: Source Code A.2. Survey A.2.1. Tasks A.2.2. Document Filter for Google Scholar A.2.3. Questionnaires A.2.4. Participant’s Answers A.2.5. Participant’s Search Result

Video Recommendations Based on Visual Features Extracted with Deep Learning

Postponed access: the file will be accessible after 2022-06-01When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list, known as Cold-start problem. This thesis investigates recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. The proposed technique has been evaluated in both offline and online evaluations using a large dataset of movies. The online evaluation has been carried out in a evaluation framework developed for this thesis. Results from the offline and online evaluation (N=150) show that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features. The results also point to subtitles as a high-quality future source of automatically extracted features. The visual feature dataset, named DeepCineProp13K and the subtitle dataset, CineSub3K, as well as the proposed evaluation framework are all made openly available online in a designated Github repository.Masteroppgave i informasjonsvitenskapINFO390MASV-INF