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Noisy Text Analytics: An Exercise in Futility?

Noisy Text Analytics: An Exercise in Futility?. Hwee Tou Ng Department of Computer Science National University of Singapore 8 Jan 2007. Noisy Text Analytics: An Exercise in Futility?. Sources of Noisy Text. Traditional sources Automatically transcribed text from speech

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Noisy Text Analytics: An Exercise in Futility?

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  1. Noisy Text Analytics: An Exercise in Futility? Hwee Tou Ng Department of Computer Science National University of Singapore 8 Jan 2007

  2. Noisy TextAnalytics: An Exercise in Futility?

  3. Sources of Noisy Text • Traditional sources • Automatically transcribed text from speech • Automatically OCRed text from image

  4. Sources of Noisy Text • More recent sources from the Web • Blogs, wikis, message boards, online chats, SMS, etc. • User generated content

  5. Sources of Noisy Text • More recent sources from the Web • Blogs, wikis, message boards, online chats, SMS, etc. • User generated content • Informal text • Acronyms, abbreviations, specialized vocabulary • Sublanguage, sub-community

  6. Importance • The rise of social media (“Web 2.0”) • Commercial, economic interest

  7. Importance • ACL SIGWAC (Special Interest Group on the Web as Corpus, Association for Computational Linguistics) • CLEANEVAL (shared task and competition for web corpus cleaning)

  8. Noisy TextAnalytics: An Exercise in Futility?

  9. An Exercise in Futility? Necessity is the mother of invention!

  10. Noisy TextAnalytics: An Exercise in Futility?

  11. What is “Analytics”? • American Heritage Dictionary • “The branch of logic dealing with analysis” • Merriam-Webster’s Online Dictionary • “The method of logical analysis”

  12. Analytics • Approach #1 • Eliminate the noise in noisy text (text normalization), followed by processing the text as per normal • Noise: Misspelled words, wrongly cased words, wrong sentence and paragraph boundaries • Examples: • Table recognition • Learning to Recognize Tables in Free Text, H T Ng, C Y Lim, J L T Koo, ACL 1999

  13. Table Recognition

  14. Table Recognition

  15. Table Recognition

  16. Analytics • Approach #2 • Process the noisy text as is directly • Examples: • Upper case text (e.g., speech recognizer output) • Teaching a Weaker Classifier: Named Entity Recognition on Upper Case Text, H L Chieu, H T Ng, ACL 2002 • Semi-structured text (e.g., seminar announcements, job advertisements) • A Maximum Entropy Approach to Information Extraction from Semi-Structured and Free Text, H L Chieu, H T Ng, AAAI 2002

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