Online research in philosophy

Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe recent empirical work on analogical cognition as well as model applications to philosophical topics. Topics to be discussed include the epistemological distinction between implicit knowledge and explicit knowledge, the debate between empiricists and nativists, the frame problem, expertise, creativity and autism, cognitive architecture, and relational knowledge. Particular attention is given to Dedre Gentner and colleague’s structure-mapping theory – the most developed and widely accepted model of analogical cognition.

This book contains an introduction to symbolic logic and a thorough discussion of mechanical theorem proving and its applications. The book consists of three major parts. Chapters 2 and 3 constitute an introduction to symbolic logic. Chapters 4–9 introduce several techniques in mechanical theorem proving, and Chapters 10 an 11 show how theorem proving can be applied to various areas such as question answering, problem solving, program analysis, and program synthesis.

mentation for languages such as Chinese. Almost no NLP task is truly standalone. The end-to-end performance of natural Most current systems for higher-level, aggre-.

This document consists primarily of excerpts (chapters 5 and 7-9) from the author’s book From Brain to Cosmos. These excerpts address some traditional philosophical problems about temporal flux and identity through time, using the concept of subjective fact that the author developed earlier in the book. (Readers unfamiliar with that concept are strongly advised to read chapters 2 and 3 of From Brain to Cosmos first. See the last page of this document for details on how to obtain those chapters.).

The 'paper' is itself an abstract, hopefully useful, of the book and its chapters from Clarendon Press (April 2006).

In this tutorial, the meaning of natural language is analysed along the lines proposed by Gottlob Frege and Richard Montague. In building meaning representations, we assume that the meaning of a complex expression derives from the meanings of its components. Typed logic is a convenient tool to make this process of composition explicit. Typed logic allows for the building of semantic representations for formal languages and fragments of natural language in a compositional way. The tutorial ends with the discussion of an example fragment, implemented in the functional programming language Haskell Haskell Team; Jones..

A method to identify ontology components is presented in this article. The method relies on Natural Language Processing (NLP) techniques to extract concepts and relations among these concepts. This method is applied in the legal field to build an ontology dedicated to information retrieval. Legal texts on which the method is performed are carefully chosen as describing and conceptualizing the legal domain. We suggest that this method can help legal ontology designers and may be used while building ontologies dedicated to other tasks than information retrieval.

This paper presents a simple and versatile tree-rewriting lexicalized grammar formalism, TAGLET, that provides an effective scaffold for introducing advanced topics in a survey course on natural language processing (NLP). Students who implement a strong competence TAGLET parser and generator simultaneously get experience with central computer science ideas and develop an effective starting point for their own subsequent projects in data-intensive and interactive NLP.

At the heart of natural language processing is the understanding of context dependent meanings. This paper presents a preliminary model of formal contexts based on situation theory. It also gives a worked-out example to show the use of contexts in lifting, i.e., how propositions holding in a particular context transform when they are moved to another context. This is useful in NLP applications where preserving meaning is a desideratum.