Natural Language Processing and Text Mining

Description

With the increasing importance of the Web and other text-heavy application areas, the demands for and interest in both text mining and natural language processing (NLP) have been rising. Researchers in text mining have hoped that NLP—the attempt to extract a fuller meaning representation from free text—can provide useful improvements to text mining applications of all kinds.

Bringing together a variety of perspectives from internationally renowned researchers, Natural Language Processing and Text Mining not only discusses applications of certain NLP techniques to certain Text Mining tasks, but also the converse, i.e., use of Text Mining to facilitate NLP. It explores a variety of real-world applications of NLP and text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, and detailing the associated difficulties that must be resolved before the algorithm can be applied and its full benefits realized. In addition, it explores a number of cutting-edge techniques and approaches, as well as novel ways of integrating various technologies. Nevertheless, even readers with only a basic knowledge of data mining or text mining will benefit from the many illustrative examples and solutions.

Topics and features:

• Describes novel and high-impact text mining and/or natural language applications

• Points out typical traps in trying to apply NLP to text mining

• Illustrates preparation and preprocessing of text data – offering practical issues and examples

• Surveys related supporting techniques, problem types, and potential technique enhancements

• Examines the interaction of text mining and NLP

This state-of-the-art, practical volume will be an essential resource for professionals and researchers who wish to learn how to apply text mining and language processing techniques to real world problems. In addition, it can be used as a supplementary text for advanced students studying text mining and NLP.

Overview.- Extracting Product Features and Opinions from Reviews.- Extracting Relations from Text.- Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles.- A Case Study in Natural Language Based Web Search.- Evaluating Self-explanations in iSTART:Word Matching, Latent Semantic Analysis, and Topic Models.- Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures.- Automatic Document Separation - A Combination of Probabilistic Classification and Finite-State Sequence Modeling.- Evolving Explanatory Novel Patterns for Semantically-based Text Mining.- Handling of Imbalanced Data in Text Classification: Category Based Term Weights.- Automatic Evaluation of Ontologies.- Linguistic Computing with UNIX Tools.- Index

描述

隨著網絡和其他以文字為主的應用領域的重要性不斷提高，對文本挖掘和自然語言處理（NLP）的需求和興趣也在增加。文本挖掘的研究人員希望NLP（從自由文本中提取更完整的意義表示的試圖）能夠為各種文本挖掘應用提供有用的改進。

《自然語言處理和文本挖掘》匯集了來自國際知名研究人員的多種觀點，不僅討論了某些NLP技術應用於某些文本挖掘任務的情況，還討論了相反的情況，即使用文本挖掘來促進NLP。它詳細探討了NLP和文本挖掘算法在各種現實應用中的應用細節，強調對真實問題的端到端解決方案的描述，並詳細介紹了在應用算法之前必須解決的相關困難以及其帶來的全部好處。此外，它還探討了一些尖端技術和方法，以及整合各種技術的新方法。然而，即使只具有基本的數據挖掘或文本挖掘知識的讀者也可以從許多示例和解決方案中受益。

主題和特點：
- 描述了新穎且具有高影響力的文本挖掘和/或自然語言應用
- 指出了在嘗試將NLP應用於文本挖掘時的典型陷阱
- 介紹了文本數據的準備和預處理，提供實際問題和示例
- 調查了相關的支持技術、問題類型和潛在技術增強
- 研究了文本挖掘和NLP的交互作用

這本最新的實用專著將成為希望學習如何將文本挖掘和語言處理技術應用於現實世界問題的專業人士和研究人員的重要資源。此外，它還可以作為高級學生學習文本挖掘和NLP的補充教材。

目錄

- 概述
- 從評論中提取產品特徵和意見
- 從文本中提取關係
- 通過學習標註知識角色來挖掘診斷性文本報告
- 自然語言為基礎的網絡搜索案例研究
- 在iSTART中評估自我解釋：單詞匹配、潛在語義分析和主題模型
- 文本簽名：使用潛在語義分析來測量文本結構的連貫性，以識別文本類型
- 自動文檔分離-概率分類和有限狀態序列建模的結合
- 為基於語義的文本挖掘演化的解釋性新模式
- 處理文本分類中的不平衡數據：基於類別的詞權重
- 本體自動評估
- 使用UNIX工具進行語言計算
- 索引