Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks

Shumin Wu, Jinho Choi, Martha Palmer


Abstract
This paper suggests a method for detecting cross-lingual semantic similarity using parallel PropBanks. We begin by improving word alignments for verb predicates generated by GIZA++ by using information available in parallel PropBanks. We applied the Kuhn-Munkres method to measure predicate-argument matching and improved verb predicate alignments by an F-score of 12.6%. Using the enhanced word alignments we checked the set of target verbs aligned to a specific source verb for semantic consistency. For a set of English verbs aligned to a Chinese verb, we checked if the English verbs belong to the same semantic class using an existing lexical database, WordNet. For a set of Chinese verbs aligned to an English verb we manually checked semantic similarity between the Chinese verbs within a set. Our results show that the verb sets we generated have a high correlation with semantic classes. This could potentially lead to an automatic technique for generating semantic classes for verbs.
Anthology ID:
2010.amta-papers.15
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
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Denver, Colorado, USA
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AMTA
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Publisher:
Association for Machine Translation in the Americas
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URL:
https://aclanthology.org/2010.amta-papers.15
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PDF:
https://aclanthology.org/2010.amta-papers.15.pdf