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Article published In: Terminology
Vol. 25:1 (2019) ► pp.93–120

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TermEnsembler

An ensemble learning approach to bilingual term extraction and alignment

 | Jožef Stefan Institute
 | Jožef Stefan Institute
 | Jožef Stefan Institute
 | Jožef Stefan Institute
 | Jožef Stefan Institute
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Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 24 July 2019
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https://doi.org/10.1075/term.00029.rep
Abstract
This paper describes TermEnsembler, a bilingual term extraction and alignment system utilizing a novel ensemble learning approach to bilingual term alignment. In the proposed system, the processing starts with monolingual term extraction from a language industry standard file type containing aligned English and Slovenian texts. The two separate term lists are then automatically aligned using an ensemble of seven bilingual alignment methods, which are first executed separately and then merged using the weights learned with an evolutionary algorithm. In the experiments, the weights were learned on one domain and tested on two other domains. When evaluated on the top 400 aligned term pairs, the precision of term alignment is over 96%, while the number of correctly aligned multi-word unit terms exceeds 30% when evaluated on the top 400 term pairs.
Article outline
  • 1.Introduction
  • 2.Related work
    • 2.1Monolingual term extraction
    • 2.2Bilingual term extraction and alignment
  • 3.TermEnsembler system and methodology
    • 3.1System overview
    • 3.2Monolingual term extraction: LUIZ-CF++ upgrade of LUIZ-CF
    • 3.3Bilingual term alignment: A novel ensemble learning approach
      • 3.3.1Individual bilingual term alignment algorithms
        • Co-frequency
        • Dice
        • Mutual information
        • BI-LUIZ+
        • Novel Phrase-Table-Based Alignment (PTBA) approaches PTBA-1, PTBA-2 and PTBA-3
      • 3.3.2Final term pair ranking by ensemble-based weighting of separate lists of term pairs
      • 3.3.3Evolutionary weighting of term alignment algorithms
  • 4.Experiments and results
    • 4.1Experimental setting
    • 4.2Data
    • 4.3Experimental comparison of individual bilingual term alignment components
      • 4.3.1Precision of individual term alignment components
      • 4.3.2Single vs. multi-word unit terms
    • 4.4Results of the TermEnsembler’s bilingual term alignment approach
      • 4.4.1Optimizing for optimal precision
      • 4.4.2Optimizing for a compromise between optimal precision and number of correct multi-word unit term pairs
      • 4.4.3Recall of the TermEnsembler system
    • 4.5Qualitative analysis of errors
  • 5.Conclusions and future work
  • Acknowledgements
  • Notes
  • References
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