A systematic comparison of various statistical alignment models
Computational Linguistics
Discovery of inference rules for question-answering
Natural Language Engineering
Corpus-based method for automatic identification of support verbs for nominalizations
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ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Paraphrasing with bilingual parallel corpora
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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Exploiting translational correspondences for pattern-independent MWE identification
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Mining complex predicates in Hindi using a parallel Hindi-English corpus
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Complex predicates annotation in a corpus of Portuguese
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Identifying and analyzing Brazilian Portuguese complex predicates
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
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We present a resource-poor approach to automatically acquire Support Verb Constructions (SVCs) for European Portuguese with a two-stage procedure. First, we apply a cross-lingual approach with a bilingual parallel corpus: starting with a Portuguese full verb, we use the translations into another language and the corresponding backtranslations to identify Portuguese verb-noun pairs with the same meaning. Since not all of these are SVCs, the candidates are ranked and filtered in a second, monolingual step based on association statistics. We discuss two parametrisations of our procedure for a high-precision and a high-recall setting. In our experiments, these parametrisations achieve a maximum precision of 91% and a maximum recall of 86%, respectively.