Improving Bilingual Lexicon Induction for Low Frequency Words

Jiaji Huang, Xingyu Cai, Kenneth Church

Machine Learning for NLP Short Paper

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Abstract: This paper designs a Monolingual Lexicon Induction task and observes that two factors accompany the degraded accuracy of bilingual lexicon induction for rare words. First, a diminishing margin between similarities in low frequency regime, and secondly, exacerbated hubness at low frequency. Based on the observation, we further propose two methods to address these two factors, respectively. The larger issue is hubness. Addressing that improves induction accuracy significantly, especially for low-frequency words.
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