Python Text Processing with NLTK 2.0 Cookbook

Jacob Perkins

  • 出版商: Packt Publishing
  • 出版日期: 2010-11-12
  • 售價: $1,550
  • 貴賓價: 9.5$1,473
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Paperback
  • ISBN: 1849513600
  • ISBN-13: 9781849513609
  • 相關分類: Python程式語言
  • 海外代購書籍(需單獨結帳)

商品描述

Use Python's NLTK suite of libraries to maximize your Natural Language Processing capabilities. * Quickly get to grips with Natural Language Processing ? with Text Analysis, Text Mining, and beyond * Learn how machines and crawlers interpret and process natural languages * Easily work with huge amounts of data and learn how to handle distributed processing * Part of Packt's Cookbook series: Each recipe is a carefully organized sequence of instructions to complete the task as efficiently as possible In Detail Natural Language Processing is used everywhere ? in search engines, spell checkers, mobile phones, computer games ? even your washing machine. Python's Natural Language Toolkit (NLTK) suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing. You want to employ nothing less than the best techniques in Natural Language Processing ? and this book is your answer. Python Text Processing with NLTK 2.0 Cookbook is your handy and illustrative guide, which will walk you through all the Natural Language Processing techniques in a step?by-step manner. It will demystify the advanced features of text analysis and text mining using the comprehensive NLTK suite. This book cuts short the preamble and you dive right into the science of text processing with a practical hands-on approach. Get started off with learning tokenization of text. Get an overview of WordNet and how to use it. Learn the basics as well as advanced features of Stemming and Lemmatization. Discover various ways to replace words with simpler and more common (read: more searched) variants. Create your own corpora and learn to create custom corpus readers for JSON files as well as for data stored in MongoDB. Use and manipulate POS taggers. Transform and normalize parsed chunks to produce a canonical form without changing their meaning. Dig into feature extraction and text classification. Learn how to easily handle huge amounts of data without any loss in efficiency or speed. This book will teach you all that and beyond, in a hands-on learn-by-doing manner. Make yourself an expert in using the NLTK for Natural Language Processing with this handy companion. What you will learn from this book * Learn Text categorization and Topic identification * Learn Stemming and Lemmatization and how to go beyond the usual spell checker * Replace negations with antonyms in your text * Learn to tokenize words into lists of sentences and words, and gain an insight into WordNet * Transform and manipulate chunks and trees * Learn advanced features of corpus readers and create your own custom corpora * Tag different parts of speech by creating, training, and using a part-of-speech tagger * Improve accuracy by combining multiple part-of-speech taggers * Learn how to do partial parsing to extract small chunks of text from a part-of-speech tagged sentence * Produce an alternative canonical form without changing the meaning by normalizing parsed chunks * Learn how search engines use Natural Language Processing to process text * Make your site more discoverable by learning how to automatically replace words with more searched equivalents * Parse dates, times, and HTML * Train and manipulate different types of classifiers Approach The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NLTK for Natural Language Processing easy and straightforward. Who this book is written for This book is for Python programmers who want to quickly get to grips with using the NLTK for Natural Language Processing. Familiarity with basic text processing concepts is required. Programmers experienced in the NLTK will also find it useful. Students of linguistics will find it invaluable.

商品描述(中文翻譯)

使用Python的NLTK套件來最大化您的自然語言處理能力。* 快速掌握自然語言處理,包括文本分析、文本挖掘等等* 學習機器和爬蟲如何解釋和處理自然語言* 輕鬆處理大量數據,並學習如何處理分散式處理* 本書是Packt的食譜系列之一:每個食譜都是一系列精心組織的指令,以最高效的方式完成任務

詳細內容
自然語言處理無處不在-在搜索引擎、拼寫檢查器、手機、電腦遊戲,甚至您的洗衣機中都有應用。Python的自然語言工具包(NLTK)套件迅速成為最高效的自然語言處理工具之一。您想要使用最佳的自然語言處理技術-這本書就是您的答案。

《Python文本處理與NLTK 2.0食譜》是您的實用指南,將以逐步方式引導您完成所有自然語言處理技術。它將以實用的實踐方式揭示文本分析和文本挖掘的高級功能,使用全面的NLTK套件。

本書將直接深入文本處理科學,以實用的實踐方式進行。首先學習文本的分詞。瞭解WordNet及其使用方法。學習詞幹提取和詞形還原的基礎和高級功能。發現用更簡單和常見(即更常搜索)的變體替換單詞的各種方法。創建自己的語料庫,並學習為JSON文件以及存儲在MongoDB中的數據創建自定義語料庫讀取器。使用和操作詞性標記器。轉換和規範解析塊以產生規範形式而不改變其含義。深入了解特徵提取和文本分類。學習如何輕鬆處理大量數據而不損失效率或速度。

本書將以實踐的方式教授您所有這些知識。通過這本實用的伴侶,使自己成為使用NLTK進行自然語言處理的專家。

您將從本書中學到什麼
* 學習文本分類和主題識別
* 學習詞幹提取和詞形還原,以及如何超越傳統的拼寫檢查器
* 在文本中用反義詞替換否定詞
* 學習將單詞分詞為句子和單詞列表,並深入瞭解WordNet
* 轉換和操作塊和樹
* 學習語料庫讀取器的高級功能,並創建自己的自定義語料庫
* 通過創建、訓練和使用詞性標記器標記不同的詞性
* 通過結合多個詞性標記器提高準確性
* 學習如何進行部分解析,從詞性標記的句子中提取小塊文本
* 通過規範化解析塊,產生替代的規範形式而不改變含義
* 學習搜索引擎如何使用自然語言處理處理文本
* 通過學習如何自動替換單詞為更常搜索的等效詞,使您的網站更易被發現
* 解析日期、時間和HTML
* 訓練和操作不同類型的分類器

方法
本書的實踐方式將使您從第一頁開始深入文本處理的核心。每個食譜都精心設計,以滿足您對自然語言處理的需求。充滿了許多實例和代碼示例,它將使使用NLTK進行自然語言處理的任務變得簡單而直接。

本書適合Python程序員,他們希望快速掌握使用NLTK進行自然語言處理。需要熟悉基本的文本處理概念。有NLTK經驗的程序員也會發現它很有用。語言學學生會發現它非常寶貴。