@inproceedings{wu-etal-2010-detecting,
    title = "Detecting Cross-lingual Semantic Similarity Using Parallel {P}rop{B}anks",
    author = "Wu, Shumin  and
      Choi, Jinho  and
      Palmer, Martha",
    booktitle = "Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers",
    month = oct # " 31-" # nov # " 4",
    year = "2010",
    address = "Denver, Colorado, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/fix-sig-urls/2010.amta-papers.15/",
    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."
}