Troubled by an inability to find any meaning in his life, the 25-year-old narrator of this deceptively simple novel quits university and eventually arrives at his brother's New York apartment. In a bid to discover what life is all about, he writes lists. He becomes obsessed by time and whether it actually matters. He faxes his meteorologist friend. He endlessly bounces a ball against the wall. He befriends a small boy who lives next door. He yearns to get to the bottom of life and how best to live it. Funny, friendly, enigmatic and frequently poignant - superbly naive.

This book introduces a theory, Naive Semantics (NS), a theory of the knowledge underlying natural language understanding. The basic assumption of NS is that knowing what a word means is not very different from knowing anything else, so that there is no difference in form of cognitive representation between lexical semantics and ency clopedic knowledge. NS represents word meanings as commonsense knowledge, and builds no special representation language (other than elements of first-order logic). The idea of teaching computers common sense knowledge originated with McCarthy and Hayes (1969), and has been extended by a number of researchers (Hobbs and Moore, 1985, Lenat et aI, 1986). Commonsense knowledge is a set of naive beliefs, at times vague and inaccurate, about the way the world is structured. Traditionally, word meanings have been viewed as criterial, as giving truth conditions for membership in the classes words name. The theory of NS, in identifying word meanings with commonsense knowledge, sees word meanings as typical descriptions of classes of objects, rather than as criterial descriptions. Therefore, reasoning with NS represen tations is probabilistic rather than monotonic. This book is divided into two parts. Part I elaborates the theory of Naive Semantics. Chapter 1 illustrates and justifies the theory. Chapter 2 details the representation of nouns in the theory, and Chapter 4 the verbs, originally published as "Commonsense Reasoning with Verbs" (McDowell and Dahlgren, 1987). Chapter 3 describes kind types, which are naive constraints on noun representations.

Written by a prominent analyst Paul. R. Halmos, this book is the most famous, popular, and widely used textbook in the subject. The book is readable for its conciseness and clear explanation. This emended edition is with completely new typesetting and corrections. Asymmetry of the book cover is due to a formal display problem. Actual books are printed symmetrically. Please look at the paperback edition for the correct image. The free PDF file available on the publisher's website www.bowwowpress.org

A Naive Realist Theory of Colour defends the view that colours are mind-independent properties of things in the environment. Keith Allen argues that a naive realist theory of colour best explains how colours appear to perceiving subjects, and that this view is not undermined by our modern scientific understanding of the world.

Contains many examples of contemporary graphic design.

A Naïve Realist Theory of Colour defends the view that colours are mind-independent properties of things in the environment, that are distinct from properties identified by the physical sciences. This view stands in contrast to the long-standing and wide-spread view amongst philosophers and scientists that colours don't really exist - or at any rate, that if they do exist, then they are radically different from the way that they appear. It is argued that a naïve realist theory of colour best explains how colours appear to perceiving subjects, and that this view is not undermined either by reflecting on variations in colour perception between perceivers and across perceptual conditions, or by our modern scientific understanding of the world. A Naïve Realist Theory of Colour also illustrates how our understanding of what colours are has far-reaching implications for wider questions about the nature of perceptual experience, the relationship between mind and world, the problem of consciousness, the apparent tension between common sense and scientific representations of the world, and even the very nature and possibility of philosophical inquiry.

This book presents recent advances (from 2008 to 2012) concerning use of the Naïve Bayes model in unsupervised word sense disambiguation (WSD). While WSD, in general, has a number of important applications in various fields of artificial intelligence (information retrieval, text processing, machine translation, message understanding, man-machine communication etc.), unsupervised WSD is considered important because it is language-independent and does not require previously annotated corpora. The Naïve Bayes model has been widely used in supervised WSD, but its use in unsupervised WSD has led to more modest disambiguation results and has been less frequent. It seems that the potential of this statistical model with respect to unsupervised WSD continues to remain insufficiently explored. The present book contends that the Naïve Bayes model needs to be fed knowledge in order to perform well as a clustering technique for unsupervised WSD and examines three entirely different sources of such knowledge for feature selection: WordNet, dependency relations and web N-grams. WSD with an underlying Naïve Bayes model is ultimately positioned on the border between unsupervised and knowledge-based techniques. The benefits of feeding knowledge (of various natures) to a knowledge-lean algorithm for unsupervised WSD that uses the Naïve Bayes model as clustering technique are clearly highlighted. The discussion shows that the Naïve Bayes model still holds promise for the open problem of unsupervised WSD.