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A data-driven approach to alternations based on protein-protein interactions


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.

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.

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.

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Additional indexing

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
Dewey Decimal Classification:000 Computer science, knowledge & systems
820 English & Old English literatures
410 Linguistics
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:06 Jun 2017 10:51
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

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