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Finding parallel texts on the web using cross-language information retrieval

Finding parallel texts on the web using cross-language information retrieval. Achim Ruopp Joint work with Fei Xia University of Washington. An early parallel text. Uses for Parallel Corpora. Parallel corpora are valuable resources for natural language processing (NLP) Machine translation

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Finding parallel texts on the web using cross-language information retrieval

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  1. Finding parallel texts on the web using cross-language information retrieval Achim Ruopp Joint work with Fei Xia University of Washington

  2. An early parallel text

  3. Uses for Parallel Corpora • Parallel corpora are valuable resources for natural language processing (NLP) • Machine translation • Cross-lingual information retrieval (IR) • E.g. PanImages from the University of Washington • Cross-lingual image search system • http://www.panimages.org/ • Computer Aided Human Translation • Monolingual NLP via information projection • …

  4. What is a Parallel Text or Parallel Corpus? • Translated text/documents in two languages • Ideally sentence-aligned (e.g. using method from Gale & Church 1993)

  5. Examples for Parallel Corpora • EUROPARL - European parliament proceedings • 10 language pairs • About 44 million words/language • Canadian parliament proceedings (Hansard) • English – French • Software documentation in multiple languages • …

  6. Motivation • Problem: Parallel corpora exist only for a limited set of language pairs • Problem: Available parallel corpora are often very domain-specific • Problem: Available parallel corpora are often small • Task: Finding parallel texts on the Web

  7. Example & Walk-Through • Previous work: • Ma and Liberman (1999) • Chen and Nie (2000) • Resnik and Smith (2003)

  8. Main Steps in Identifying Parallel Text on the Web (Resnik and Smith, 2003) • Locating pages that might have parallel translations • Generating candidate page pairs that might be translations • Filtering out of non-translation candidate pairs

  9. Our approach • Locating pages that might have parallel translations: • Sampling by sending queries • Generating candidate page pairs that might be translations: • Comparing URLs with different matching methods • Filtering out of non-translation candidate pairs: • Combining structural and content-based filtering

  10. System Overview

  11. Outline • System description (1a) Sampling the source language L1 (1b) Checking pages in the target language L2 (2) Matching pages in L1 and L2 (3) Filtering page pairs • Experiments • Conclusion and future work

  12. (1a) One-term Sample • Sample • Search engine query of one term • Limited to pages in source language • Optional parameter: inurl:<2-letter language ID> • Submitted to search engine API • Search engine does automatic stemming • 100 pages in result set

  13. (1a) Choosing Terms • Dictionary • Built using Giza++ word alignment tool • Trained on years 2001-2003 of the Europarl corpus • Contains IBM Model 1 translation probabilities • Sampling term • Selected from source language vocabulary • Mid-frequency term • Selected at random using a normal distribution • Goal: Avoid domain-specificity

  14. (1a) Source Language Expansion • From one-term to n-term queries • Common IR query expansion technique • Based on page summaries returned by the one-term sampling query • Summary terms ranked by frequency • Leads to semantically related terms because of relevancy ranking of search engine results “shannon” → “information claude” “inconveniences” → “security travelers” • Original term expanded with one or more expansion terms re-submitted to search engine

  15. (1b) Checking Query • Sampling query terms translated using the Giza++ dictionary “inconvenience security travelers” → “unannehmlichkeit sicherheit” • m-best translations of n sampling terms lead to mn checking queries • Optional parameter: inurl:<2-letter language ID>

  16. (1b) Target language expansion • Alternative to translating a complete n-term sampling query • Only translate original one-term sample • Expand on target language side equivalently as on source language side • m checking queries instead of mn • Efficiency vs. source language expansion evaluated in experiments

  17. (1b) “site:” Parameter • Optional: site parameter • Allows sites retrieved in checking query to be restricted to sites returned in sampling query • Search engine limits to sites of first 30 sampling query page results

  18. (2) Matching URLs with Fixed Language List • URLs from corresponding sampling and checking result sets • Considered a match if they only differ in a in a fixed list of language IDs

  19. (2) Matching URLs with Levenshtein Distance • Levenshtein distance • Also known as “edit distance” • URLs from corresponding sampling and checking result sets • Considered a match when URLs have a Levenshtein distance less or equal than 4, but larger than 0 http://ec.europa.eu/education/policies/rec_qual/recognition/diploma_en.html http://ec.europa.eu/education/policies/rec_qual/recognition/diploma_de.html

  20. (2) URL part substitution • Sampling L1  source URLs • Replacing L1 names/ids in each source URL with L2 names/ids  target URLs • Checking whether the target URLs exist • Does not require checking queries!

  21. (3) Filtering page pairs • Structural filtering (Resnik and Smith, 2003) • Content translation metric (Ma and Liberman, 1999) • Linear combination

  22. (3) Linearization Linearized File HTML file [START:HTML] [START:HEAD] [START:META] [Chunk: 12] [END:META] [START:TITLE] [Chunk:25] [END:TITLE] [END:HTML] <HTML> <HEAD> <META> … </META> <TITLE> ….. </TITLE> </HTML>

  23. (3) Alignment Linearized Source File Linearized Target File [START:HTML] [START:HEAD] [START:META] [END:META] [START:LINK] [END:LINK] [START:TITLE] [Chunk:68] [END:TITLE] [START:META] … [START:HTML] [START:HEAD] [START:META] [END:META] [START:TITLE] [Chunk:58] [END:TITLE] [START:META] …

  24. (3) Structural Metrics • Difference percentage (dp) • Measures how different markup in linearized files is • Based on longest common subsequence algorithm (Hunt & McIlroy 1976) • Implemented using diff tool • Length correlation of aligned non-markup chunks (r) • Pearson correlation coefficient over all aligned chunks in a file pair • Length of content in characters

  25. (3) Content Translation Metric • Calculated on first 500 content words on page • Using the Giza++ translation dictionary

  26. (3) Combining Two Kinds of Metrics • Structural metrics: dp and r • Content-based metric: c • Linear combination:

  27. Different settings for experiments (1a) Sampling the source language L1 • Source expansion • The “inurl:” parameter (1b) Checking pages in the target language L2 • Target expansion • The “inurl:” and “site:” parameter • Matching pages in L1 and L2 • Using a fixed list • Edit distance • URL part substitution

  28. Experiment Results – Matches

  29. Observations – Sampling and Checking • Query expansion increases the number of page pairs • Source and target query expansion lead to similar results • Difference between n=2 and n=3 is not significant • Possible explanation: Larger semantic divergence of queries on the source and target language sides • Using site: and inurl: search parameters increases the number of discovered page pairs • But: structural parameters might miss candidate pairs that don’t follow pattern

  30. Observations – Matching • Number of page pair candidates • URL part substitution >> Levensthein distance • Levenshtein distance > Fixed language list • Matching methods that use checking queries are heavily impacted by relevancy rankings • Levenshtein distance matching method • Allows learning of URL patterns used for parallel pages

  31. Experiment Results – Filtering

  32. Observations – Filtering • Combined filter • Evaluated in comparison to human judge • Precision “How many did we get right?” • 88.9% • Encouraging on noisy test set • Recall “How many did we miss?” • 36.4% • Low recall can be compensated for by submitting more queries

  33. Conclusions • It is possible reliably gather parallel pages using commercial search engines • Even though there are no standard features identifying these pages • Despite the relevance ranking of commercial search engine results

  34. Future work • To improve the precision and recall of the filtering step. • To address the relevancy ranking and the page limit problem • To study whether some queries are more productive than others • To test the usefulness of the collected page pairs on applications such as MT.

  35. Additional Slides

  36. Experiments & Results

  37. Languages on the Web Source: http://www.glreach.com/globstats/index.php3

  38. Questions Asked • How do we find parallel pages in the sea of mostly monolingual pages? • What is the share of parallel pages for a given language pair?

  39. Estimating the Percentage of Parallel Pages for a Language Pair P(DE|E)=0.03% P(ED|D)=0.27%

  40. References • IJCNLP 2008 paper and presentation • http://search.iiit.ac.in/CLIA2008/accepted_papers.php • Email • mailto:achim@digitalsilkroad.net • MSR internship information • http://research.microsoft.com/aboutmsr/jobs/internships/default.aspx

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