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Identification of multiword expressions: A fresh look at modelling and evaluation
Taslimipoor, Shiva Rohanian, Omid Mitkov, Ruslan Fazly, Afsaneh
Taslimipoor, Shiva
Rohanian, Omid
Mitkov, Ruslan
Fazly, Afsaneh
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2018-10-25
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Shiva Taslimipoor, Omid Rohanian, Ruslan Mitkov & Afsaneh Fazly. 2018. Identification of multiword expressions: A fresh look at modelling and evaluation. In Stella Markantonatou, Carlos Ramisch, Agata Savary & Veronika Vincze (eds.), Multiword expressions at length and in depth: Extended papers from the MWE 2017 workshop, 299– 317. Berlin: Language Science Press. DOI:10.5281/zenodo.1469569
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en
Description
Automatic identification of Multiword Expressions (MWEs) in running text has
recently received much attention among researchers in computational linguistics.
The wide range of reported results for the task in the literature has prompted us to
take a closer look at the algorithms and evaluation methods. For supervised classification of Verb+Noun expressions, we propose a context-based methodology in
which we find word embeddings to be appropriate features. We discuss the importance of train and test splitting in validating the results and present type-aware
train and test splitting. Given our specialised data, we also discuss the benefits of
framing the task as classification rather than tagging.
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9783961101245
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Attribution-NonCommercial-NoDerivs 3.0 United States