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Web Scale NLP: A Case Study on URL Word Breaking

Web Scale NLP: A Case Study on URL Word Breaking. Kuansan Wang, Chris Thrasher, Bo-June (Paul) Hsu Microsoft Research, Redmond, USA WWW 2011 March 31, 2011. More Data > Complex Model. Banko and Brill. Mitigating the Paucity-of-Data Problems . HLT 01. More Data > Complex Model. ?.

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Web Scale NLP: A Case Study on URL Word Breaking

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  1. Web Scale NLP:A Case Study on URL Word Breaking Kuansan Wang, Chris Thrasher, Bo-June (Paul) Hsu Microsoft Research, Redmond, USA WWW 2011 March 31, 2011

  2. More Data > Complex Model Banko and Brill. Mitigating the Paucity-of-Data Problems. HLT 01

  3. More Data > Complex Model ? There is no data like more data • CIKM 08

  4. NLP for the Web Simple models with matcheddata! • Scale of the Web • Avoid manual intervention • Efficient implementations • Dynamic Nature of the Web • Fast adaptation • Global Reach of the Web • Need rudimentary multi-lingual capabilities • Diverse Language Styles of Web Contents • Multi-style language models

  5. Outline Web-Scale NLP Word Breaking Models Evaluation Conclusion

  6. Word Breaking • Large Data + Simple Model (Norvig, CIKM 2008) • Use unigram model to rank all possible segmentations • Pretty good, but with occasional embarrassing outcomes • More data does not help! • Extension to trigram alleviates the problem

  7. Word Breaking for the Web Matcheddata is crucial to accuracy! Web URLs exhibit variety of language styles… …and in different languages

  8. Outline Web-Scale NLP Word Breaking Models Evaluation Conclusion

  9. MAP Decision Rule Distortion Channel Signal Observation • Special case of Bayesian Minimum Risk • Speech, MT, Parsing, Tagging, Information Retrieval, … • Problem: Given , find : transformation model : prior

  10. MAP for Word Breaker Transformation Channel Signal Output • : tweeter hash tag or URL domain name • Ex. 247moms, w84um8 • : what user meant to say • Ex. 24_7_moms, w8_4_u_m8 (wait for you mate)

  11. Plug-in MAP Problem • MAP decision rule is optimal only if and are the “correct” underlying distributions • Adjustments needed when estimated models and have unknown errors • Simple logarithmic interpolation: • “Random Field”/Machine Learning: • Bayesian • Point estimation is outdated • Assume parameters are drawn from “some” distribution

  12. Baseline Methods All special cases/variations of MAP • GM: Geometric Mean (Keohn and Kline, 2003) • Widely used, especially in MT systems • BI: Binomial Model (Venkataraman, 2001) • WL: Word Length Normalization (Kaitan et al, 2009)

  13. Proposed Method ME: Maximum Entropy Principle Model – Special case of BI () and WL (uniform) using Microsoft Web N-gram, Microsoft Web N-gram (http://web-ngram.research.microsoft.com) Web documents/Bing queries (EN-US market) Rudimentary multilingual (NAACL 10) Frequent updates (ICASSP 09) Multi-style language model (WWW 10, SIGIR 10)

  14. Outline Web-Scale NLP Word Breaking Models Evaluation Conclusion

  15. Data Set • 100K randomly sampled URLs indexed by Bing • Simple tokenization • 266K unique tokens • Mostly ASCII characters • Metric: Precision@3 • Manually labeled word breaks • Multiple answers are allowed

  16. Language Model Style Matchedstyle is crucial to precision! • Title is best although Body is 100x larger • Nav queries often word-split URLs, but Query worse than Title

  17. Model Complexity Simplemodel is sufficient with matcheddata! • With mismatched data, model choice is crucial • With matched data, complex models do not help

  18. Outline Web-Scale NLP Word Breaking Models Evaluation Conclusion

  19. Best = Right Data + Smart Model • Style of language trumps size of data • There is no data like more data…provided it’s matched data! • Right data alleviates Plug-in MAP problem • Complicated machine learning artillery not required; simple methods suffice • Smart model gives us: • Rudimentary multi-lingual capability • Fast inclusion of new words/phrases • Eliminate needs of human labor in data labeling http://research.microsoft.com/en-us/um /people/kuansanw/wordbreaker/

  20. Backup Slides

  21. Title Query Anchor Note: BI, WL are oracle results

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