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.

Inferencing is defined as 'the act of deriving logical conclusions from premises known or assumed to be true', and it is one of the most important processes necessary for successful comprehension during reading. This volume features contributions by distinguished researchers in cognitive psychology, educational psychology, and neuroscience on topics central to our understanding of the inferential process during reading. The chapters cover aspects of inferencing that range from the fundamental bottom-up processes that form the basis for an inference to occur, to the more strategic processes that transpire when a reader is engaged in literary understanding of a text. Basic activation mechanisms, word-level inferencing, methodological considerations, inference validation, causal inferencing, emotion, development of inferences processes as a skill, embodiment, contributions from neuroscience, and applications to naturalistic text are all covered as well as expository text, online learning materials, and literary immersion.

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).

Chart Sense for Writing is the companion to the best-selling Chart Sense: Common Sense Charts to Teach 3-8 Informational Text and Literature. This resource is for elementary and middle school teachers who are ready to create meaningful, standards-based charts with their students. The same charts that Rozlyn creates with students when she models and teaches writing in classrooms across the nation are all included here. Packed with over seventy photographs, Chart Sense for Writing is an invaluable guide for novice or veteran teachers who want authentic visuals to reinforce and provide guidance for the writing classroom. Organized in a simple, easy-to-use format, Rozlyn shares multiple charts for each writing standard. At over 190 pages, this book is filled with actual charts, step-by-step instructions to create your own, teaching tips, and instructional strategies.

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.

No detailed description available for "Text Processing / Textverarbeitung".

This work provides an overview of a wide range of approaches to written text analysis. It includes both classic and specially commissioned papers by distinguished authors, which share a common linguistic framework. The pieces contain a variety of focuses from the patterning of paragraphs, sections or whole texts to the organization of clauses, individual expressions and single words, as well as a variety of text-types. The examples used range from pure science through social science, academic journals, weekly magazines and newspapers, to literary narratives. This collection forms the basis for an course on written text analysis that should be of interest to advanced undergraduate and postgraduate students.

This book constitutes the refereed proceedings of the 9th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2008, held in Haifa, Israel, in February 2008. The 52 revised full papers presented together with 4 invited papers were carefully reviewed and selected from numerous submissions. The papers cover all current issues in computational linguistics research and present intelligent text processing applications. The papers are organized in topical sections on language resources, morphology and syntax, semantics and discourse, word sense disambiguation and named entity recognition, anaphora and co-reference, machine translation and parallel corpora, natural language generation, speech recognition, information retrieval and question answering, text classification, text summarization, as well as spell checking and authoring aid.