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Author: Linda Wetzel
Publisher: MIT Press
ISBN: 0262266148
Category : Philosophy
Languages : en
Pages : 197
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Book Description
A consideration of the distinction between the spatiotemporal furniture of the world (tokens) and the types of which they are instances, and an argument that types exist. There is a widely recognized but infrequently discussed distinction between the spatiotemporal furniture of the world (tokens) and the types of which they are instances. Words come in both types and tokens—for example, there is only one word type 'the' but there are numerous tokens of it on this page—as do symphonies, bears, chess games, and many other types of things. In this book, Linda Wetzel examines the distinction between types and tokens and argues that types exist (as abstract objects, since they lack a unique spatiotemporal location). Wetzel demonstrates the ubiquity of references to (and quantifications over) types in science and ordinary language; types have to be reckoned with, and cannot simply be swept under the rug. Wetzel argues that there are such things as types by undermining the epistemological arguments against abstract objects and offering extended original arguments demonstrating the failure of nominalistic attempts to paraphrase away such references to (and quantifications over) types. She then focuses on the relation between types and their tokens, especially for words, showing for the first time that there is nothing that all tokens of a type need have in common other than being tokens of that type. Finally, she considers an often-overlooked problem for realism having to do with types occurring in other types (such as words in a sentence) and proposes an important and original solution, extending her discussion from words and expressions to other types that structurally involve other types (flags and stars and stripes; molecules and atoms; sonatas and notes).
Author: Linda Wetzel
Publisher: MIT Press
ISBN: 0262266148
Category : Philosophy
Languages : en
Pages : 197
Get Book Here
Book Description
A consideration of the distinction between the spatiotemporal furniture of the world (tokens) and the types of which they are instances, and an argument that types exist. There is a widely recognized but infrequently discussed distinction between the spatiotemporal furniture of the world (tokens) and the types of which they are instances. Words come in both types and tokens—for example, there is only one word type 'the' but there are numerous tokens of it on this page—as do symphonies, bears, chess games, and many other types of things. In this book, Linda Wetzel examines the distinction between types and tokens and argues that types exist (as abstract objects, since they lack a unique spatiotemporal location). Wetzel demonstrates the ubiquity of references to (and quantifications over) types in science and ordinary language; types have to be reckoned with, and cannot simply be swept under the rug. Wetzel argues that there are such things as types by undermining the epistemological arguments against abstract objects and offering extended original arguments demonstrating the failure of nominalistic attempts to paraphrase away such references to (and quantifications over) types. She then focuses on the relation between types and their tokens, especially for words, showing for the first time that there is nothing that all tokens of a type need have in common other than being tokens of that type. Finally, she considers an often-overlooked problem for realism having to do with types occurring in other types (such as words in a sentence) and proposes an important and original solution, extending her discussion from words and expressions to other types that structurally involve other types (flags and stars and stripes; molecules and atoms; sonatas and notes).
Author: Jerzy Pelc
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110824043
Category : Language Arts & Disciplines
Languages : en
Pages : 520
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Book Description
No detailed description available for "Sign, System and Function".
Author: Alex Martelli
Publisher: "O'Reilly Media, Inc."
ISBN: 0596100469
Category : Computers
Languages : en
Pages : 734
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Book Description
This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions.
Author: Rod Gardner
Publisher: John Benjamins Publishing
ISBN: 9027251118
Category : Language Arts & Disciplines
Languages : en
Pages : 309
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Book Description
Listeners are usually considered recipients in conversational interaction, whose main activity is to take in messages from other speakers. In this view, the listening activity is separate from speaking. Another view is that listeners and speakers are equal co-participants in conversations who construct the talk together. In support of this latter view, one finds a group of vocalisations which are quintessentially listener talk little conversational objects such as uh-huh, oh, mm, yeah, right and mm-hm. These utterances do not have meanings in a conventional dictionary sense, but are nevertheless loaded with complex and subtle information about the stance listeners take to what they are hearing, information that is gleaned not only from their phonetic form, but also from their complex prosodic shape and their placement and timing within the flow of talk. This book summarises eight of these objects, and explores one, mm, in depth.
Author: Emil Hvitfeldt
Publisher: CRC Press
ISBN: 1000461971
Category : Computers
Languages : en
Pages : 402
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Book Description
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Author: Steven Bird
Publisher: "O'Reilly Media, Inc."
ISBN: 0596555717
Category : Computers
Languages : en
Pages : 506
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Book Description
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.
Author: Rochelle Lieber
Publisher: Cambridge University Press
ISBN: 0521895499
Category : Language Arts & Disciplines
Languages : en
Pages : 229
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Book Description
A lively introduction to the study of how words are put together.
Author: Vaclav Brezina
Publisher: Cambridge University Press
ISBN: 1107125707
Category : Foreign Language Study
Languages : en
Pages : 317
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Book Description
A comprehensive and accessible introduction to statistics in corpus linguistics, covering multiple techniques of quantitative language analysis and data visualisation.
Author: Sylvain Bromberger
Publisher:
ISBN:
Category : Computational linguistics
Languages : en
Pages : 42
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Book Description
Author: Ankur A. Patel
Publisher: "O'Reilly Media, Inc."
ISBN: 1492062545
Category : Computers
Languages : en
Pages : 336
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Book Description
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production
