Natural languages have various ways of expressing quantification, such as the English words "some" and "all." Different such words exist in different languages, and the same word can communicate quite different quantities depending on the context. This thesis presents a computational framework for modeling quantificational meanings and their use in communication. The model can represent meanings that depend on absolute amounts (e.g., two) as well as relative amounts (e.g., half of the total) and context-dependent amounts. It can also represent meanings with presuppositions. Communication between a speaker and a listener is modeled as single exchanges in which both participants have noisy perception of the actual state of the world, the speaker tries to communicate some quantity to the listener by using some word chosen to be informative, and the listener tries to infer the quantity using the word and the assumption that the speaker was being informative. The usage patterns predicted by the model are qualitatively similar to how the words are actually used. The model also shows that the sets of words in real languages result in more efficient communication than randomly selected sets of words with comparable meanings.

The ideal of using human language to control machines requires a practical theory of natural language communication that includes grammatical analysis of language signs, plus a model of the cognitive agent, with interfaces for recognition and action, an internal database, and an algorithm for reading content in and out. This book offers a functional framework for theoretical analysis of natural language communication and for practical applications of natural language processing.

A nontechnical introduction to complexity theory: its strengths, its weaknesses, and how it can be used to study grammars.

This open access book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data. This computational cognitive modeling approach opens up exciting new directions for research in formal semantics, and linguistics more generally, and offers new ways of (re)connecting semantics and the broader field of cognitive science. The approach of this book is novel in more ways than one. Assuming the mental architecture and procedural modalities of Anderson's ACT-R framework, it presents fine-grained computational models of human language processing tasks which make detailed quantitative predictions that can be checked against the results of self-paced reading and other psycho-linguistic experiments. All models are presented as computer programs that readers can run on their own computer and on inputs of their choice, thereby learning to design, program and run their own models. But even for readers who won't do all that, the book will show how such detailed, quantitatively predicting modeling of linguistic processes is possible. A methodological breakthrough and a must for anyone concerned about the future of linguistics! (Hans Kamp) This book constitutes a major step forward in linguistics and psycholinguistics. It constitutes a unique synthesis of several different research traditions: computational models of psycholinguistic processes, and formal models of semantics and discourse processing. The work also introduces a sophisticated python-based software environment for modeling linguistic processes. This book has the potential to revolutionize not only formal models of linguistics, but also models of language processing more generally. (Shravan Vasishth) .

A review of recent computational (deep learning) approaches to understanding news and nonfiction stories.

This extended collection of papers is the result of putting recent ideas on quantification to work on a wide variety of languages. A central perspective of many of the papers follows the recognition of two broad types of quantificational strategies, one associated with nominal structures and determiners, the other with adverbial and other non-nominal expression (`D-quantifiers' and `A-quantifiers'). The papers demonstrate both the unity and the variety of natural language quantificational forms and meanings. Many of the papers also shed new light on questions of language typology and syntactic and morphological variation. The languages discussed include English, Dutch, Italian, American Sign Language, Hindi, and a number of languages of Australia, Greenland, and the Americas. These comparative studies provide initial data for a typology of quantificational structures in natural languages, with important implications for the study of universal grammar. The book consists of research papers aimed at linguists, philosophers, and psychologists interested in semantics and linguistic form. An introduction presents a sketch of the background of this research and some of the central issues discussed, with pointers toward the included papers.

This book highlights cutting-edge research relevant to the building of a computational model of reading comprehension, as in the processing and understanding of a natural language text or story. The book takes an interdisciplinary approach to the study of reading, with contributions from computer science, psychology, and philosophy. Contributors cover the theoretical and psychological foundations of the research in discussions of what it means to understand a text, how one builds a computational model, and related issues in knowledge representation and reasoning. The book also addresses some of the broader issues that a natural language system must deal with, such as reading in context, linguistic novelty, and information extraction.

Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more. The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important. - Provides a thorough treatment of open-source libraries, application frameworks and workflow systems for natural language analysis and understanding - Presents new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, and more