Thesis (Ph. D.)--University of Chicago, Dept. of Anthropology, March 2003. / Includes bibliographical references. Also available on the Internet.

Inaug.-Diss.--Göttingen. / Lebenslauf. "Literatur": p. v-vii.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1975. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 157-162).

Inaug.-Diss.--Frankfurt a. M. / Lebenslauf. "Literatur-verzeichnis": p. 110-112.

No description available.

Texte remanié de: Th. doct.--Sci. soc.--Paris 7, 1996. / Bibliogr. p. 475-480.

Texte remanié de: PhD thesis. / Bibliogr. p. 331-354. Index.

We address the challenging task of computational natural language inference, by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question with its answer and we learn to approximate this inference with machine learning. In particular, here we present four approaches to question answering, each of which shows a significant improvement in performance over baseline methods. In our first approach, we make use of the underlying discourse structure inherent in free text (i.e. whether the text contains an explanation, elaboration, contrast, etc.) in order to increase the amount of training data for (and subsequently the performance of) a monolingual alignment model. In our second work, we propose a framework for training customized lexical semantics models such that each one represents a single semantic relation. We use causality as a use case, and demonstrate that our customized model is able to both identify causal relations as well as significantly improve our ability to answer causal questions. We then propose two approaches that seek to answer questions by learning to rank human-readable justifications for the answers, such that the model selects the answer with the best justification. The first uses a graph-structured representation of the background knowledge and performs information aggregation to construct multi-sentence justifications. The second reduces pre-processing costs by limiting itself to a single sentence and using a neural network to learn a latent representation of the background knowledge. For each of these, we show that in addition to significant improvement in correctly answering questions, we also outperform a strong baseline in terms of the quality of the answer justification given.

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. Keywords:automated question answering, FAQ answering,question-answering system, template-based question answering,question template, natural language based interface / <p>NR 20140805</p>

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