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        <title>Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks</title>
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    <name type="personal">
        <namePart type="given">Shumin</namePart>
        <namePart type="family">Wu</namePart>
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        <namePart type="given">Jinho</namePart>
        <namePart type="family">Choi</namePart>
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    <name type="personal">
        <namePart type="given">Martha</namePart>
        <namePart type="family">Palmer</namePart>
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        <dateIssued>2010-oct 31-nov 4</dateIssued>
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            <title>Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers</title>
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            <publisher>Association for Machine Translation in the Americas</publisher>
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    <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.</abstract>
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        <url>https://aclanthology.org/2010.amta-papers.15</url>
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        <date>2010-oct 31-nov 4</date>
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%0 Conference Proceedings
%T Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks
%A Wu, Shumin
%A Choi, Jinho
%A Palmer, Martha
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F wu-etal-2010-detecting
%X 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.
%U https://aclanthology.org/2010.amta-papers.15

Markdown (Informal)

[Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks](https://aclanthology.org/2010.amta-papers.15) (Wu et al., AMTA 2010)

• Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks (Wu et al., AMTA 2010)

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.