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
Address:
Denver, Colorado, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
Language:
URL:
https://aclanthology.org/2010.amta-papers.15
DOI:
Bibkey:
Cite (ACL):
Shumin Wu, Jinho Choi, and Martha Palmer. 2010. Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks (Wu et al., AMTA 2010)
Copy Citation:
PDF:
https://aclanthology.org/2010.amta-papers.15.pdf