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Mining the Web for Minority Language Corpora: Methods and Results

This paper discusses the methodology for creating language corpora for minority languages using web mining techniques. It addresses the challenges faced by languages with limited resources and the importance of language-specific corpora for applications such as language modeling, speech recognition, and machine translation. By utilizing search engines and relevant filtering algorithms, the study demonstrates effective ways to quickly generate corpora with minimal manual effort. The results show promising outcomes for languages like Slovenian, Croatian, Czech, and Tagalog, and outline future research directions.

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Mining the Web for Minority Language Corpora: Methods and Results

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  1. Mining the Web to Create Minority Language Corpora Rayid GhaniAccenture Technology Labs - Research Rosie Jones Carnegie Mellon University Dunja Mladenic J. Stefan Institute, Slovenia

  2. Who Needs a Language Specific Corpus? • Language Technology Applications • Language Modeling • Speech Recognition • Machine Translation • Linguistic and Socio-Linguistic Studies • Multilingual Retrieval

  3. What Corpora are Available? • Explicit, marked up corpora: Linguistic Data Consortium -- 20 languages [Liebermann and Cieri 1998] • Search Engines -- implicit language-specific corpora, European languages, Chinese and Japanese • Excite - 12 languages • Google - 25 languages • AltaVista - 25 languages • Lycos - 25 languages

  4. You’re just out of luck! BUT what about Slovenian? Or Tagalog? Or Tatar?

  5. The Human Solution • Start from Yahoo->Slovenia… • Crawl www.*.si • Search on the web, look at documents, modify query, analyze documents, modify query,… • Repetitive, time-consuming, requires reasonable familiarity with the language

  6. Task • Given: • 1 Document in Target Language • 1 Other Document (negative example) • Access to a Web Search Engine • Create a Corpus of the Target Language quickly with no human effort

  7. Algorithm Query Generator WWW Seed Docs Language Filter

  8. Build Query Learning Web Initial Docs Word Statistics Relevant Filter Non-Relevant

  9. Query Generation • Examine current relevant and non-relevant documents to generate a query likely to find documents that ARE similar to the relevant ones and NOT similar to non-relevant ones • A Query consists of minclusion terms and nexclusion terms • e.g +intelligence +web –military

  10. Query Term Selection Methods • Uniform (UN) – select k words randomly from the current vocabulary • Term-Frequency (TF) – select top k words ranked according to their frequency • Probabilistic TF (PTF) – k words with probability proportional to their frequency

  11. Query Term Selection Methods • RTFIDF – top k words according to their rtfidf scores • Odds-Ratio (OR) – top k words according to their odds-ratio scores • Probabilistic OR (POR) – select k words with probability proportional to their Odds-Ratio scores

  12. Evaluation • Goal: Collect as many relevant documents as possible while minimizing the cost • Cost • Number of totaldocumentsretrieved from the Web • Number of distinct Queries issued to the Search Engine • Evaluation Measures • Percentage of retrieved documents that are relevant • Number of relevant documents retrieved per unique query

  13. Experimental Setup • Language: Slovenian • Initial documents: 1 web page in Slovenian, 1 in English • Search engine: Altavista

  14. Results

  15. Results – Precision at 3000 Percentage of Target Docs after 3000 Docs Retrieved

  16. Results – Docs Per Query

  17. Results - Summary • In terms of documents: • For lengths 1-3, Odds-Ratio works best • In terms of queries: • Odds-Ratio is consistently better than others • Long queries are usually very precise but do not result in a lot of documents (low recall)

  18. Further Experiments • Comparison to Altavista’s “More Like This” • Better performance than Altavista’s feature • Keywords • Similar results when initializing with keywords instead of documents • Other Languages • Similar results with Croatian, Czech and Tagalog

  19. Conclusions • Successfully able to build corpora for minority languages (Slovenian, Croatian, Czech, Tagalog) using Web search engines • Not sensitive to initial “seed” documents • System and Corpora are/will be available at www.cs.cmu.edu/~TextLearning/CorpusBuilder

  20. Ideas for Future Work • Explore other Term-Selection methods • From Language specific corpus to Topic Specific corpus as an alternative to focused spidering • Finding documents matching a user profile – Personal Agent

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