December 1979, volume 24 There is a distinct tradition in the literature on social science research methods that advocates the use of multiple methods. This form of research strategy is usually described as one of convergent methodology, multimethod/multitrait (Campbell and Fiske, 1959), convergent validation or, what has been called "triangulation" (Webb et al., 1 966). These various notions share the conception that qualitative and quantitative methods should be viewed as complementary rather than as rival camps. In fact, most textbooks underscore the desirability of mixing methods given the strengths and weaknesses found in single method designs.

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Mixing Qualitative and Quantitative Methods: Triangulation in Action @

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

https://www.foo.be/cours/dess-20122013/b/Natural%20Language%20Processing%20with%20Python%20-%20O'Reilly2009.pdf

Natural Language Processing with Python

語用論(Pragmatics)を考える

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

The Cambridge Grammar of the English Language

Genericity: An Introduction

This work consists of an introduction and 11 articles on important aspects of the interpretation of generic expressions. The introduction provides a clear overview of various issues and synthesizes the major analytical approaches to them. Taken together, the papers that follow reflect the current state of the art in the semantics of generics, and afford insight into various generic phenomena.

The Generic book

This book introduces fundamental techniques for computing semantic representations for fragments of natural language and performing inference with the result. The prinary tools used are first-order logic and lambda calculus. All the techniques introduced are implemented in Prolog. The book also shown how to use theorem provers and model builders in parallel to deal with natrual language inference.

Representation and Inference for Natural Language: A First Course in Computational Semantics

Fuzzy logic methods have been used successfully in many real-world applications, but the foundations of fuzzy logic remain under attack. Taken together, these two facts constitute a paradox. A second paradox is that almost all of the successful fuzzy logic applications are embedded controllers, while most of the theoretical papers on fuzzy methods deal with knowledge representation and reasoning. I hope to resolve these paradoxes by identifying which aspects of fuzzy logic render it useful in practice, and which aspects are inessential. My conclusions are based on a mathematical result, on a survey of literature on the use of fuzzy logic in heuristic control and in expert systems, and on practical experience in developing expert systems. >

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The paradoxical success of fuzzy logic

The purpose of this note is to provide a graduated selection of problems for use in testing an automatic theorem proving (ATP) system. Included in these problems were the ones I have used in testing my own ATP system (Pelletier 1982, and more recent updates to the system). Some of the problems, especially some of the more difficult ones described at the end of this note, are due to discussions with Len Schubert (Computing, Univ. Alberta), Alasdair Urquhart (Philosophy, Univ. Toronto), and Charles Morgan (Philosophy, Univ. Victoria). People who have tried to compile lists of problems for ATPs in the past (for example, the Association for Automated Reasoning) have discovered that the pro- duction of such a list is difficult because (a) what is ‘easy’ for one system might not be for another, (b) researchers are understandably shy about saying what problems their ATP might have, unless they know that it is so difficult that any ATP will have trouble with it, (c) especially with problems at the ‘easy end’ of the scale, researchers are prone to think that any ATP system can prove it and so there is no call to write them up, (d) the goals of ‘natural’ system ATPs and resolution-based ATPs are different: the former tries to produce a ‘natural’ proof usually without prior conver- sion to clause form. This means that some problems, especially at the ‘easy end’ of the scale, will be trivial for resolution systems but difficult for ‘natural’ systems. On the other hand, proponents of ‘natural’ systems think that at the ‘difficult end’ of the scale, there will be problems within the grasp of the ‘natural’ systems which are beyond the reach of resolution systems. It is therefore difficult to even give a graduated scale of problems. All this has led to publication of dificuft problems, but not to publication of lists of problems suitable for developing an ATP. The locus clussicus of problems is McCharen et al. (1976) which contains a wide range of problems all in clause form. More recently, the Journal of Automated Reasoning has instituted its ‘Problem Corner’. A notable item herein is Lusk and Overbeek (1985). All of us involved in ATP wish to encourage others - graduate students, for example - to look into the field. But where is such a person to start? It is with such neophyte

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Francis Jeffry Pelletier

Papers

Genericity: An Introduction

The Generic book

Representation and Inference for Natural Language: A First Course in Computational Semantics

The paradoxical success of fuzzy logic

Seventy-five problems for testing automatic theorem provers