Hungarian Particle Verbs in a Corpus-Driven Approach

2018 ◽  
pp. 123-133
Author(s):  
Ágnes Kalivoda
Keyword(s):  
Particle Verbs

When morphological structure overrides meaning: evidence from German prefix and particle verbs

2018 ◽  
Vol 34 (5) ◽  
pp. 599-614 ◽  
Author(s):  
Eva Smolka ◽  
Gary Libben ◽  
Wolfgang U. Dressler
Keyword(s):  
Morphological Structure ◽  
Particle Verbs

scholarly journals Distinguishing Literal and Non-Literal Usage of German Particle Verbs

2016 ◽  
Author(s):  
Maximilian Köper ◽  
Sabine Schulte im Walde
Keyword(s):  
Particle Verbs

Particle Verbs in English

10.1075/la.59 ◽  
2002 ◽  
Author(s):  
Nicole Dehé
Keyword(s):  
Particle Verbs

Particle Verbs and Local Domains

10.1075/la.41 ◽  
2001 ◽  
Author(s):  
Jochen Zeller
Keyword(s):  
Particle Verbs

scholarly journals Large-Scale Induction and Evaluation of Lexical Resources from the Penn-II and Penn-III Treebanks

2005 ◽  
Vol 31 (3) ◽  
pp. 329-366 ◽  
Author(s):  
Ruth O'Donovan ◽  
Michael Burke ◽  
Aoife Cahill ◽  
Josef van Genabith ◽  
Andy Way
Keyword(s):  
Large Scale ◽  
Lexical Resources ◽  
Long Distance ◽  
Complete Evaluation ◽  
Lexical Functional Grammar ◽  
Particle Verbs ◽  
Source Data ◽  
Complete Set ◽  
Syntactic Function ◽  
Structure Annotation

We present a methodology for extracting subcategorization frames based on an automatic lexical-functional grammar (LFG) f-structure annotation algorithm for the Penn-II and Penn-III Treebanks. We extract syntactic-function-based subcategorization frames (LFG semantic forms) and traditional CFG category-based subcategorization frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle information for particle verbs. Our approach associates probabilities with frames conditional on the lemma, distinguishes between active and passive frames, and fully reflects the effects of long-distance dependencies in the source data structures. In contrast to many other approaches, ours does not predefine the subcategorization frame types extracted, learning them instead from the source data. Including particles and prepositions, we extract 21,005 lemma frame types for 4,362 verb lemmas, with a total of 577 frame types and an average of 4.8 frame types per verb. We present a large-scale evaluation of the complete set of forms extracted against the full COMLEX resource. To our knowledge, this is the largest and most complete evaluation of subcategorization frames acquired automatically for English.

Sign in / Sign up

Export Citation Format

Share Document