Natural Language Annotation for Machine Learning (Paperback)

James Pustejovsky, Amber Stubbs

Natural Language Annotation for Machine Learning (Paperback)

買這商品的人也買了...

相關主題

商品描述

Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.

Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project.

  • Define a clear annotation goal before collecting your dataset (corpus)
  • Learn tools for analyzing the linguistic content of your corpus
  • Build a model and specification for your annotation project
  • Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework
  • Create a gold standard corpus that can be used to train and test ML algorithms
  • Select the ML algorithms that will process your annotated data
  • Evaluate the test results and revise your annotation task
  • Learn how to use lightweight software for annotating texts and adjudicating the annotations

This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.

商品描述(中文翻譯)

創建自己的自然語言訓練語料庫，用於機器學習。無論您是使用英語、中文還是其他任何自然語言，這本實踐指南將引導您完成一個經過驗證的註釋開發週期，即向訓練語料庫添加元數據以幫助機器學習算法更高效地工作的過程。您無需任何編程或語言學經驗即可開始。

通過詳細的示例，您將學習到MATTER註釋開發過程如何幫助您對訓練語料庫進行Model、Annotate、Train、Test、Evaluate和Revise。您還將完整地了解一個真實註釋項目的步驟。


  • 在收集數據集（語料庫）之前，明確定義註釋目標

  • 學習分析語料庫語言內容的工具

  • 為註釋項目構建模型和規範

  • 檢查不同的註釋格式，從基本的XML到語言註釋框架

  • 創建一個可以用於訓練和測試機器學習算法的黃金標準語料庫

  • 選擇處理註釋數據的機器學習算法

  • 評估測試結果並修訂註釋任務

  • 學習如何使用輕量級軟件進行文本註釋和仲裁註釋

這本書是O'Reilly的Python自然語言處理的完美伴侶。

類似商品