@article{tambouratzis2014d,
author = "Tambouratzis, George",
abstract = "This communication focuses on comparing the template-matching technique to established probabilistic approaches - such as Conditional Random Fields (CRF) - on a specific linguistic task, namely the phrasing of a sequence of words into phrases. This task represents a low-level parsing of the sequence into linguistically-motivated phrases. CRF represents the established method for implementing such a data-driven parser, while template-matching is a simpler method that is faster to train and operate. The two aforementioned techniques are compared here to determine the most suitable approach for extracting an accurate model. 
",
doi = "in print",
issn = "0167-8655",
journal = "Pattern Recognition Letters",
keywords = "parsing of natural language ; template-matching; Conditional-random fields; phrasing model generator; machine translation",
number = "in print",
title = "{C}onditional {R}andom {F}ields versus template-matching in {M}T phrasing tasks involving sparse training data ",
volume = "in print",
year = "2014",
}