Δημοσίευση - Conditional Random Fields versus template-matching in MT phrasing tasks involving sparse training data
ΤΑΥΤΟΤΗΤΑ

Conditional Random Fields versus template-matching in MT phrasing tasks involving sparse training data

Ερευνητική περιοχή:  
Άλλα θέματα Πληροφορικής
    
Είδος:  
Άρθρο σε περιοδικό

 

Έτος: 2014
Συγγραφείς: Γιώργος Ταμπουρατζής
Περιοδικό: Pattern Recognition Letters
Τόμος: in print
Αριθμός: in print
ISSN: 0167-8655
DOI: in print
Περίληψη:
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.
[Bibtex]