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【书名】：Pragmatics: A Slim Guide

【作者】：Betty J. Birner

【年份】：2021年

【出版社】：牛津大学出版社

【简介】：【团购】语用学入门小手册

This book offers a concise but comprehensive entry-level guide to the study of meaning in context. There can be a big difference between what a speaker says and what they mean - i.e. between literal meaning and intended meaning. A speaker who says I need coffee can mean anything from 'Please

buy more coffee' to 'I'm really sleepy'. How is a hearer to know? In this book, Betty Birner explores how we get from what is said to what is meant, from the perspective of both the speaker and the hearer, dealing with a range of context-dependent issues in language along the way: literal and

non-literal meaning, implicature, speech acts, reference, definiteness, presupposition, and information structure. She reveals how language users can infer each other's meanings using not just what is being said but also the context and an assumption of rationality and cooperation.

This slim guide summarizes the most important and foundational theories in the field of linguistic pragmatics, illustrated with plenty of real-life examples, and including a helpful glossary of key terms. Written in a lively and accessible style, the book will appeal to a wide range of readers, from

undergraduate and graduate students of pragmatics to general readers interested in how we successfully communicate with one another.

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【书名】：Natural Language Processing and Computational Linguistics: A Practical Guide to Text Analysis With Python, Gensim, spaCy, and Keras

【作者】：Bhargav Srinivasa-Desikan

【年份】：2018年

【出版社】：Packt出版社

【简介】：自然语言处理和计算语言学：使用Python，Gensim，spaCy和Keras进行文本分析的实用指南

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.

This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.

You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.

This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

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