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Generating natural language text in response to questions about database structure /McKeown, Kathleen R. January 1900 (has links)
Thesis (Ph. D.)--University of Pennsylvania, 1982. / Cover title. Includes bibliographical references and index.
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TERRESA a task-based message-driven parallel semantic network system /Lee, Chain-Wu. January 1900 (has links)
Thesis (Ph. D.)--State University of New York at Buffalo, 1999. / "January 25, 1999." Includes bibliographical references (leaves 201-209). Also available in print.
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Model selection based speaker adaptation and its application to nonnative speech recognition /He, Xiaodong, January 2003 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 99-110). Also available on the Internet.
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Model selection based speaker adaptation and its application to nonnative speech recognitionHe, Xiaodong, January 2003 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 99-110). Also available on the Internet.
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Global models for temporal relation classificationPonvert, Elias Franchot 17 January 2013 (has links)
Temporal relation classification is one of the most challenging areas of natural language processing. Advances in this area have direct relevance to improving practical applications, such as question-answering and summarization systems, as well as informing theoretical understanding of temporal meaning realization in language. With the development of annotated textual materials, this domain is now accessible to empirical machine-learning oriented approaches, where systems treat temporal relation processing as a classification problem: i.e. a decision as per which label (before, after, identity, etc) to assign to a pair (i, j) of event indices in a text. Most reported systems in this new research domain utilize classifiers that make decisions effectively in isolation, without explicitly utilizing the decisions made about other indices in a document. In this work, we present a new strategy for temporal relation classification that utilizes global models of temporal relations in a document, choosing the optimal classification for all pairs of indices in a document subject to global constraints which may be linguistically motivated. We propose and evaluate two applications of global models to temporal semantic processing: joint prediction of situation entities with temporal relations, and temporal relations prediction guided by global coherence constraints. / text
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Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data miningTang, Lap Poon Rupert 28 August 2008 (has links)
Not available / text
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Learning for information extraction: from named entity recognition and disambiguation to relation extractionBunescu, Razvan Constantin, 1975- 28 August 2008 (has links)
Information Extraction, the task of locating textual mentions of specific types of entities and their relationships, aims at representing the information contained in text documents in a structured format that is more amenable to applications in data mining, question answering, or the semantic web. The goal of our research is to design information extraction models that obtain improved performance by exploiting types of evidence that have not been explored in previous approaches. Since designing an extraction system through introspection by a domain expert is a laborious and time consuming process, the focus of this thesis will be on methods that automatically induce an extraction model by training on a dataset of manually labeled examples. Named Entity Recognition is an information extraction task that is concerned with finding textual mentions of entities that belong to a predefined set of categories. We approach this task as a phrase classification problem, in which candidate phrases from the same document are collectively classified. Global correlations between candidate entities are captured in a model built using the expressive framework of Relational Markov Networks. Additionally, we propose a novel tractable approach to phrase classification for named entity recognition based on a special Junction Tree representation. Classifying entity mentions into a predefined set of categories achieves only a partial disambiguation of the names. This is further refined in the task of Named Entity Disambiguation, where names need to be linked to their actual denotations. In our research, we use Wikipedia as a repository of named entities and propose a ranking approach to disambiguation that exploits learned correlations between words from the name context and categories from the Wikipedia taxonomy. Relation Extraction refers to finding relevant relationships between entities mentioned in text documents. Our approaches to this information extraction task differ in the type and the amount of supervision required. We first propose two relation extraction methods that are trained on documents in which sentences are manually annotated for the required relationships. In the first method, the extraction patterns correspond to sequences of words and word classes anchored at two entity names occurring in the same sentence. These are used as implicit features in a generalized subsequence kernel, with weights computed through training of Support Vector Machines. In the second approach, the implicit extraction features are focused on the shortest path between the two entities in the word-word dependency graph of the sentence. Finally, in a significant departure from previous learning approaches to relation extraction, we propose reducing the amount of required supervision to only a handful of pairs of entities known to exhibit or not exhibit the desired relationship. Each pair is associated with a bag of sentences extracted automatically from a very large corpus. We extend the subsequence kernel to handle this weaker form of supervision, and describe a method for weighting features in order to focus on those correlated with the target relation rather than with the individual entities. The resulting Multiple Instance Learning approach offers a competitive alternative to previous relation extraction methods, at a significantly reduced cost in human supervision. / text
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Learning for semantic parsing with kernels under various forms of supervisionKate, Rohit Jaivant, 1978- 28 August 2008 (has links)
Not available / text
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Learning for semantic parsing and natural language generation using statistical machine translation techniquesWong, Yuk Wah, 1979- 28 August 2008 (has links)
Not available
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A computational model of language pathology in schizophreniaGrasemann, Hans Ulrich 07 February 2011 (has links)
No current laboratory test can reliably identify patients with schizophrenia. Instead,
key symptoms are observed via language, including derailment, where patients cannot follow
a coherent storyline, and delusions, where false beliefs are repeated as fact. Brain
processes underlying these and other symptoms remain unclear, and characterizing them
would greatly enhance our understanding of schizophrenia. In this situation, computational
models can be valuable tools to formulate testable hypotheses and to complement clinical
research. This dissertation aims to capture the link between biology and schizophrenic
symptoms using DISCERN, a connectionist model of human story processing. Competing
illness mechanisms proposed to underlie schizophrenia are simulated in DISCERN,
and are evaluated at the level of narrative language, the same level used to diagnose patients.
The result is the first simulation of a speaker with schizophrenia. Of all illness
models, hyperlearning, a model of overly intense memory consolidation, produced the best
fit to patient data, as well as compelling models of delusions and derailments. If validated
experimentally, the hyperlearning hypothesis could advance the current understanding of
schizophrenia, and provide a platform for simulating the effects of future treatments. / text
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