Inferences In Text Processing
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Inferences In Text Processing
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Author : H. Strohl-Goebel
language : en
Publisher: Elsevier
Release Date : 1985-11-01
Inferences In Text Processing written by H. Strohl-Goebel and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-11-01 with Psychology categories.
This volume critically evaluates the present state of research in the domain of inferences in text processing and indicates new areas of research. The book is structured around the following theoretical aspects: - The representational aspect is concerned with the cognitive structure produced by the processed text, e.g. the social, spatial, and motor characteristics of world knowledge. - The procedural aspect investigates the time relationships on forming inferences, e.g. the point of time at which referential relations are constructed. - The contextual aspect reflects the dependence of inferences on the communicative embedding of text processing, e.g. on factors of modality and instruction.
Text Processing And Text Comprehension According To Walter Kintsch
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Author : Saskia Bachner
language : en
Publisher: GRIN Verlag
Release Date : 2008-09-04
Text Processing And Text Comprehension According To Walter Kintsch written by Saskia Bachner and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-04 with Biography & Autobiography categories.
Seminar paper from the year 2007 in the subject English Language and Literature Studies - Linguistics, grade: 1,3, University of Mannheim, course: Psycholinguistics, 17 entries in the bibliography, language: English, abstract: Reading is a part of our daily life. It enables us to get information, for example when we read a newspaper, or it is just for entertainment. Once we have learned to read, we are not able to stop it anymore. If we see a text, we read it automatically and know what it means. But how is it possible that we understand the meaning of a text? What is going on inside our brain while we are reading? And how are we able to remember and recall something from a text? These are central questions the text processing research concentrates on. In order to find an answer to them, researchers have different approaches. One of them is the construction-integration model by Walter Kintsch, which has its origin in several earlier models of processing. The main field of application for this model is instruction. The results of research on learning can be used to create new instruction methods, which facilitate the process of learning and advance the ability to remember what has just been learned. My term paper is going to concentrate on Kintsch's construction-integration model and its assumptions. It is structured into two parts. The first part gives an overview of the theory. To be able to understand the model, I will initially describe its different components, namely: propositions, the text base, the situation model, and inferences (chapter 2). Then, I will briefly dwell on Kintsch's earlier models (chapter 3). Afterwards, I will explain the model itself and give a short evaluation of it in chapter 4. The second part of the term paper consists of my imitation of an experiment on the existence of propositions, which was originally carried out by Gail McKoon and Roger Ratcliff (chapter 5).
Textual Inference For Machine Comprehension
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Author : Martin Gleize
language : en
Publisher:
Release Date : 2016
Textual Inference For Machine Comprehension written by Martin Gleize and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
With the ever-growing mass of published text, natural language understanding stands as one of the most sought-after goal of artificial intelligence. In natural language, not every fact expressed in the text is necessarily explicit: human readers naturally infer what is missing through various intuitive linguistic skills, common sense or domain-specific knowledge, and life experiences. Natural Language Processing (NLP) systems do not have these initial capabilities. Unable to draw inferences to fill the gaps in the text, they cannot truly understand it. This dissertation focuses on this problem and presents our work on the automatic resolution of textual inferences in the context of machine reading. A textual inference is simply defined as a relation between two fragments of text: a human reading the first can reasonably infer that the second is true. A lot of different NLP tasks more or less directly evaluate systems on their ability to recognize textual inference. Among this multiplicity of evaluation frameworks, inferences themselves are not one and the same and also present a wide variety of different types. We reflect on inferences for NLP from a theoretical standpoint and present two contributions addressing these levels of diversity: an abstract contextualized inference task encompassing most NLP inference-related tasks, and a novel hierchical taxonomy of textual inferences based on their difficulty.Automatically recognizing textual inference currently almost always involves a machine learning model, trained to use various linguistic features on a labeled dataset of samples of textual inference. However, specific data on complex inference phenomena is not currently abundant enough that systems can directly learn world knowledge and commonsense reasoning. Instead, systems focus on learning how to use the syntactic structure of sentences to align the words of two semantically related sentences. To extend what systems know of the world, they include external background knowledge, often improving their results. But this addition is often made on top of other features, and rarely well integrated to sentence structure. The main contributions of our thesis address the previous concern, with the aim of solving complex natural language understanding tasks. With the hypothesis that a simpler lexicon should make easier to compare the sense of two sentences, we present a passage retrieval method using structured lexical expansion backed up by a simplifying dictionary. This simplification hypothesis is tested again in a contribution on textual entailment: syntactical paraphrases are extracted from the same dictionary and repeatedly applied on the first sentence to turn it into the second. We then present a machine learning kernel-based method recognizing sentence rewritings, with a notion of types able to encode lexical-semantic knowledge. This approach is effective on three tasks: paraphrase identification, textual entailment and question answering. We address its lack of scalability while keeping most of its strengths in our last contribution. Reading comprehension tests are used for evaluation: these multiple-choice questions on short text constitute the most practical way to assess textual inference within a complete context. Our system is founded on a efficient tree edit algorithm, and the features extracted from edit sequences are used to build two classifiers for the validation and invalidation of answer candidates. This approach reaches second place at the "Entrance Exams" CLEF 2015 challenge.
The Use Of Three Types Of Inferences In Text Processing In Cantonese Speaking Children Aged 4 7 7 0
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Author : Sin-yee Wong (Ivy)
language : en
Publisher:
Release Date : 1994
The Use Of Three Types Of Inferences In Text Processing In Cantonese Speaking Children Aged 4 7 7 0 written by Sin-yee Wong (Ivy) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Cantonese dialects categories.
Superficial Processing Of Explicit Inferences In Text
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Author : Rand J. Spiro
language : en
Publisher:
Release Date : 1977
Superficial Processing Of Explicit Inferences In Text written by Rand J. Spiro and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with Human information processing categories.
Text Analysis For The Social Sciences
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Author : Carl W. Roberts
language : en
Publisher: Routledge
Release Date : 2020-07-24
Text Analysis For The Social Sciences written by Carl W. Roberts and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-24 with Language Arts & Disciplines categories.
This book provides descriptions and illustrations of cutting-edge text analysis methods for communication and marketing research; cultural, historical-comparative, and event analysis; curriculum evaluation; psychological diagnosis; language development research; and for any research in which statistical inferences are drawn from samples of texts. Although the book is accessible to readers having no experience with content analysis, the text analysis expert will find substantial new material in its pages. In particular, this collection describes developments in semantic and network text analysis methodologies that heretofore have been accessible only among a smattering of methodology journals. The book's international and cross-disciplinary content illustrates the breadth of quantitative text analysis applications. These applications demonstrate the methods' utility for international research, as well as for practitioners from the fields of sociology, political science, journalism/communication, computer science, marketing, education, and English. This is an "ecumenical" collection that contains applications not only of the most recent semantic and network text analysis methods, but also of the more traditional thematic method of text analysis. In fact, it is originally with this volume that these two "relational" approaches to text analysis are defined and contrasted with more traditional "thematic" text analysis methods. The emphasis here is on application. The book's chapters provide guidance regarding the sorts of inferences that each method affords, and up-to-date descriptions of the human and technological resources required to apply the methods. Its purpose is as a resource for making quantitative text analysis methods more accessible to social science researchers.
Grammatical Inference Algorithms And Applications
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Author : Arlindo L. Oliveira
language : en
Publisher: Springer
Release Date : 2004-02-13
Grammatical Inference Algorithms And Applications written by Arlindo L. Oliveira and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-02-13 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Colloquium on Grammatical Inference, ICGI 2000, held in Lisbon, Portugal in September 2000. The 24 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers address topics like machine learning, automata, theoretical computer science, computational linguistics, pattern recognition, artificial neural networks, natural language acquisition, computational biology, information retrieval, text processing, and adaptive intelligent agents.
Coarse To Fine Natural Language Processing
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Author : Slav Petrov
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-03
Coarse To Fine Natural Language Processing written by Slav Petrov and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-03 with Computers categories.
The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing. Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing. Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach. Eugene Charniak (Brown University)
Mathematical Language Processing
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Author : Deborah Mendes Ferreira
language : en
Publisher:
Release Date : 2022
Mathematical Language Processing written by Deborah Mendes Ferreira and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Predictive Inferences During Text Comprehension
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Author : Douglas A. Waring
language : en
Publisher:
Release Date : 1994
Predictive Inferences During Text Comprehension written by Douglas A. Waring and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Comprehension categories.
