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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-52960

Schneider, Gerold; Rinaldi, Fabio (2011). A data-driven approach to alternations based on protein-protein interactions. In: III Congreso Internacional de Lingüística de Corpus, Valencia, Spain, 7 April 2011 - 9 April 2011, 597-607.

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Abstract

Syntactic alternations like the dative shift are well researched. But most decisions
which speakers take are more complex than binary choices. Multifactorial lexicogrammatical
approaches and a large inventory of syntactic patterns are needed to
supplement current approaches. We use the term semantic alternation for the many
ways in which a relation between entities, conveying broadly the same meaning, can be
expressed. We use a well-resourced domain, biomedical research texts, for a corpusdriven
approach. As entities we use proteins, and as relations we use interactions between
them, using Text Mining training data. We discuss three approaches: first, manually
designed syntactic patterns, second a corpus-based semi-automatic approach and
third a machine-learning language model. The machine-learning approach learns the
probability that a syntactic configuration expresses a relevant interaction from an annotated
corpus. The inventory of configurations define the envelope of variation and its
multitude of forms.

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > English Department
06 Faculty of Arts > Institute of Computational Linguistics
DDC:820 English & Old English literatures
410 Linguistics
000 Computer science, knowledge & systems
Uncontrolled Keywords:syntactic alternations lexicogrammar corpus-driven semantic alternation text mining machine learning
Language:English
Event End Date:9 April 2011
Deposited On:06 Jan 2012 15:24
Last Modified:12 Sep 2012 16:32
Publisher:Universitat Politècnica de València
ISBN:978-84-694-6225-6
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.upv.es/pls/obib/sic_publ.FichPublica?P_ARM=6032
Citations:Google Scholar™

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