Cette recherche propose d’examiner les marqueurs de l’anglais à la lumière du concept de polyphonie, abondamment utilisé en linguistique du français. Cette étude du cas de l’interro-négative en anglais questionne le caractère opératoire de ce concept : renseigne-t-il aussi le fonctionnement de la langue anglaise ?Pour répondre à cette question, nous proposons tout d’abord un retour sur les bases théoriques qui ont inspiré notre travail, pour les mettre ensuite à l’épreuve de nos occurrences d’interro-négatives extraites de notre corpus de nouvelles de Raymond Carver. Nous analysons ainsi, dans un premier temps, les interro-négatives sans pronom interrogatif, introduites par isn’t, don’t et didn’t. Dans un deuxième temps, nous portons notre attention sur la question ouverte introduite par le pronom interrogatif why. Enfin, les question-tags sont abordées : elles permettent d’asseoir notre thèse quant à la place importante qu’occupe l’interlocuteur dans la construction de message. A cet égard, nous réhabilitons son rôle dans le processus de construction de message en lui attribuant celui de co-constructeur / This research examines English linguistic markers using the concept of ‘polyphony’, i.e. intersubjectivity, a concept that is key to many studies in French linguistics. More precisely, we examine how negative interrogative constructions in English work, while also exploring the possibility that they may shed light on how the English linguistic system functions as a whole.To do so, we begin by reviewing the theoretical work that inspired our study. This earlier work is then applied to our corpus of negative interrogatives, which were sourced from a collection of short stories by Raymond Carver. Our analysis begins by looking at negative interrogatives that do not contain interrogative pronouns – more precisely, those introduced by isn’t, don’t and didn’t. Then, we focus our attention on wh-questions introduced by the interrogative pronoun why. Finally, we look at tag questions: this allows us to anchor our work in a place that accords primary importance to the interlocutor in the construction of linguistic messages. As far as this is concerned, we redefine the interlocutor’s role in this process by considering them a co-constructor of the linguistic message

Visual Question Answering (VQA) models should have both high robustness and accuracy. Unfortunately, most of the current VQA research only focuses on accuracy because there is a lack of proper methods to measure the robustness of VQA models. There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the ranked basic questions, with similarity scores, of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question about the given image. We claim that a robust VQA model is one, whose performance is not changed much when related basic questions as also made available to it as input. We formulate the basic questions generation problem as a LASSO optimization, and also propose a large scale Basic Question Dataset (BQD) and Rscore (novel robustness measure), for analyzing the robustness of VQA models. We hope our BQD will be used as a benchmark for to evaluate the robustness of VQA models, so as to help the community build more robust and accurate VQA models.

Open-domain question answering (QA) is an established NLP task which enables users to search for speciVc pieces of information in large collections of texts. Instead of using keyword-based queries and a standard information retrieval engine, QA systems allow the use of natural language questions and return the exact answer (or a list of plausible answers) with supporting snippets of text. In the past decade, open-domain QA research has been dominated by evaluation fora such as TREC and CLEF, where shallow techniques relying on information redundancy have achieved very good performance. However, this performance is generally limited to simple factoid and deVnition questions because the answer is usually explicitly present in the document collection. Current approaches are much less successful in Vnding implicit answers and are diXcult to adapt to more complex question types which are likely to be posed by users. In order to advance the Veld of QA, this thesis proposes a shift in focus from simple factoid questions to encyclopaedic questions: list questions composed of several constraints. These questions have more than one correct answer which usually cannot be extracted from one small snippet of text. To correctly interpret the question, systems need to combine classic knowledge-based approaches with advanced NLP techniques. To Vnd and extract answers, systems need to aggregate atomic facts from heterogeneous sources as opposed to simply relying on keyword-based similarity. Encyclopaedic questions promote QA systems which use basic reasoning, making them more robust and easier to extend with new types of constraints and new types of questions. A novel semantic architecture is proposed which represents a paradigm shift in open-domain QA system design, using semantic concepts and knowledge representation instead of words and information retrieval. The architecture consists of two phases, analysis – responsible for interpreting questions and Vnding answers, and feedback – responsible for interacting with the user. This architecture provides the basis for EQUAL, a semantic QA system developed as part of the thesis, which uses Wikipedia as a source of world knowledge and iii employs simple forms of open-domain inference to answer encyclopaedic questions. EQUAL combines the output of a syntactic parser with semantic information from Wikipedia to analyse questions. To address natural language ambiguity, the system builds several formal interpretations containing the constraints speciVed by the user and addresses each interpretation in parallel. To Vnd answers, the system then tests these constraints individually for each candidate answer, considering information from diUerent documents and/or sources. The correctness of an answer is not proved using a logical formalism, instead a conVdence-based measure is employed. This measure reWects the validation of constraints from raw natural language, automatically extracted entities, relations and available structured and semi-structured knowledge from Wikipedia and the Semantic Web. When searching for and validating answers, EQUAL uses the Wikipedia link graph to Vnd relevant information. This method achieves good precision and allows only pages of a certain type to be considered, but is aUected by the incompleteness of the existing markup targeted towards human readers. In order to address this, a semantic analysis module which disambiguates entities is developed to enrich Wikipedia articles with additional links to other pages. The module increases recall, enabling the system to rely more on the link structure of Wikipedia than on word-based similarity between pages. It also allows authoritative information from diUerent sources to be linked to the encyclopaedia, further enhancing the coverage of the system. The viability of the proposed approach was evaluated in an independent setting by participating in two competitions at CLEF 2008 and 2009. In both competitions, EQUAL outperformed standard textual QA systems as well as semi-automatic approaches. Having established a feasible way forward for the design of open-domain QA systems, future work will attempt to further improve performance to take advantage of recent advances in information extraction and knowledge representation, as well as by experimenting with formal reasoning and inferencing capabilities.

No description available.

Despite the importance of the Slave Trade Question in European diplomacy from 1807-1822, historians of this period have neglected it in order to concentrate on Napoleon and the reconstruction of Europe. Scholars of Negro history generally have traced the slave trade up to 1807 and then have turned to the emancipation movement. This thesis represents an attempt to satisfy the need for a diplomatic study of this issue.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Les systèmes de question-réponse renvoient une réponse précise à une question formulée en langue naturelle. Les systèmes de question-réponse actuels, ainsi que les campagnes d'évaluation les évaluant, font en général l'hypothèse qu'une seule réponse est attendue pour une question. Or nous avons constaté que, souvent, ce n'était pas le cas, surtout quand on cherche les réponses sur le Web et non dans une collection finie de documents.Nous nous sommes donc intéressés au traitement des questions attendant plusieurs réponses à travers un système de question-réponse sur le Web en français. Pour cela, nous avons développé le système Citron capable d'extraire des réponses multiples différentes à des questions factuelles en domaine ouvert, ainsi que de repérer et d'extraire le critère variant (date, lieu) source de la multiplicité des réponses. Nous avons montré grâce à notre étude de différents corpus que les réponses à de telles questions se trouvaient souvent dans des tableaux ou des listes mais que ces structures sont difficilement analysables automatiquement sans prétraitement. C'est pourquoi, nous avons également développé l'outil Kitten qui permet d'extraire le contenu des documents HTML sous forme de texte et aussi de repérer, analyser et formater ces structures. Enfin, nous avons réalisé deux expériences avec des utilisateurs. La première expérience évaluait Citron et les êtres humains sur la tâche d'extraction de réponse multiples : les résultats ont montré que Citron était plus rapide que les êtres humains et que l'écart entre la qualité des réponses de Citron et celle des utilisateurs était raisonnable. La seconde expérience a évalué la satisfaction des utilisateurs concernant la présentation de réponses multiples : les résultats ont montré que les utilisateurs préféraient la présentation de Citron agrégeant les réponses et y ajoutant un critère variant (lorsqu'il existe) par rapport à la présentation utilisée lors des campagnes d'évaluation. / Question answering systems find and extract a precise answer to a question in natural language. Both current question-answering systems and evaluation campaigns often assume that only one single answeris expected for a question. Our corpus studies show that this is rarely the case, specially when answers are extracted from the Web instead of a frozen collection of documents.We therefore focus on questions expecting multiple correct answers fromthe Web by developping the question-answering system Citron. Citron is dedicated to extracting multiple answers in open domain and identifying theshifting criteria (date, location) which is often the reason of this answer multiplicity Our corpus studies show that the answers of this kind of questions are often located in structures such as tables and lists which cannot be analysed without a suitable preprocessing. Consequently we developed the Kitten software which aims at extracting text information from HTML documents and also both identifying and formatting these structures.We finally evaluate Citron through two experiments involving users. Thefirst experiment evaluates both Citron and human beings on a multipleanswer extraction task: results show that Citron was faster than humans andthat the quality difference between answers extracted by Citron andhumans was reasonable. The second experiment evaluates user satisfaction regarding the presentation of multiple answers: results show that user shave a preference for Citron presentation aggregating answers and adding the shifting criteria (if it exists) over the presentation used by evaluation campaigns.

No description available.

Thesis (M.A.) in Communication--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 135-142).

<p>The rapid growth in the development of Internet-basedinformation systems increases the demand for natural langu-ageinterfaces that are easy to set up and maintain. Unfortunately,the problem of understanding natural language queries is farfrom being solved. Therefore this research proposes a simplertask of matching a one-sentence-long user question to a numberof question templates, which cover the knowledge domain of theinformation system, without in-depth understanding of the userquestion itself.The research started with development of an FAQ(Frequently Asked Question) answering system that providespre-stored answers to user questions asked in ordinary English.The language processing technique developed for FAQ retrievaldoes not analyze user questions. Instead, analysis is appliedto FAQs in the database long before any user questions aresubmitted. Thus, the work of FAQ retrieval is reduced tokeyword matching without understanding the questions, and thesystem still creates an illusion of intelligence.Further, the research adapted the FAQ answering techniqueto a question-answering interface for a structured database,e.g., relational database. The entity-relationship model of thedatabase is covered with an exhaustive collection of questiontemplates - dynamic, parameterized "frequently asked questions"- that describe the entities, their attributes, and therelationships in form of natural language questions. Unlike astatic FAQ, a question template contains entity slots - freespace for data instances that represent the main concepts inthe question. In order to answer a user question, the systemfinds matching question templates and data instances that fillthe entity slots. The associated answer templates create theanswer.Finally, the thesis introduces a generic model oftemplate-based question answering which is a summary andgene-ralization of the features common for the above systems:they (i) split the application-specific knowledge domain into anumber of question-specific knowledge domains, (ii) attach aquestion template, whose answer is known in advance, to eachknowledge domain, and (iii) match the submitted user questionto each question template within the context of its ownknowledge domain.</p><p><b>Keywords:</b>automated question answering, FAQ answering,question-answering system, template-based question answering,question template, natural language based interface</p>