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Evaluating a pivot-based approach for bilingual lexicon extraction.

Jae-Hoon Kim1, Hong-Seok Kwon1, Hyeong-Won Seo1

  • 1Department of Computer Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 606-791, Republic of Korea.

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Summary
This summary is machine-generated.

This study validates a pivot-based approach for bilingual lexicon extraction, proving its effectiveness for low-resource languages and infrequent words. The method uses context vector similarity for accurate cross-lingual word translations.

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Lexicography

Background:

  • Bilingual lexicon extraction is crucial for cross-lingual applications.
  • Traditional methods often struggle with low-resource languages.
  • Pivot-based approaches offer a promising alternative.

Purpose of the Study:

  • To evaluate the validity and usability of a pivot-based approach for bilingual lexicon extraction.
  • To compare two distinct methods for estimating context vectors within the pivot approach.
  • To demonstrate the approach's effectiveness for resource-poor languages and low-frequency words.

Main Methods:

  • Utilized a pivot-based strategy relying on context vector similarity.
  • Estimated context vectors using word association between source/target and pivot words.
  • Estimated context vectors using word alignment tools from statistical machine translation.

Main Results:

  • The pivot-based approach demonstrated significant promise for resource-poor language pairs (Korean-Spanish, Korean-French).
  • The approach confirmed its validity and usability in practical lexicon extraction.
  • The method performed well even for words with low frequency.

Conclusions:

  • The pivot-based approach is a viable and effective method for bilingual lexicon extraction, especially for languages with limited resources.
  • The evaluated methods for context vector estimation support the robustness of the pivot-based strategy.
  • This approach enhances cross-lingual information retrieval and machine translation capabilities for under-represented languages.