D15-1191 |
This paper proposes context-dependent KG
embedding
, a twostage scheme that takes into account both types of connectivity patterns and obtains more accurate embeddings .
|
J80-1001 |
ATN 's have the advantage of being a class of automata into which ordinary context-free phrase structure and " augmented " phrase structure grammars have a straightforward
embedding
, but which permit various transformations to be performed to produce grammars that can be more efficient than the original .
|
W15-3820 |
Such word vector representations , also known as word
embedding
, have been shown to improve the performance of machine learning models in several NLP tasks .
|
J00-3002 |
Discussing such center
embedding
, Johnson ( 1998 ) presents the essential idea developed here , noting that processing overload of dependencies invoked in psycholinguistic literature could be rendered in terms of the maximal number of unresolved dependencies as represented by proof nets .
|
C00-1055 |
We have experimented with lnore elaborate functions that indicate how balanced the parse tree is and less complicated functions such as the level of
embedding
, number of parentheses , and so oil .
|
W14-4002 |
Inspired by work on parsing ( Klein and Man - ning , 2003 ) , we explore a vertical Markovian labeling approach : intuitively , 0th-order labels signify the reordering of the sub-phrases inside the phrase pair ( Zhang et al. , 2008 ) , 1st-order labels signify reordering aspects of the direct context ( an
embedding
, parent phrase pair ) of the phrase pair , and so on .
|
J00-3002 |
We argue that an incremental procedure of proof net construction affords an account of various processing phenomena , including garden pathing , the unacceptability of center
embedding
, preference for lower attach - ment , left-to-right quantifier scope preference , and heavy noun phrase shift .
|
P13-2087 |
We propose a method that takes as input an existing
embedding
, some labeled data , and produces an embedding in the same space , but with a better predictive performance in the supervised task .
|
D14-1015 |
To employ the contextual information , we propose a simple and memory-efficient model for learning bilingual
embedding
, taking both the source phrase and context around the phrase into account .
|
W15-4310 |
Besides word
embedding
, we use partof-speech ( POS ) tags , chunks , and brown clusters induced from Wikipedia as fea - tures .
|
W10-4104 |
The discourse structures in other examples of this section are
embedding
, while this example is of overlapping type .
|
C92-2095 |
( = S ) on tile right and so eliminate unnecessary center -
embedding
; and ( 3 ) eliminating of scrambling and NP drop to isolate tile separate effects of llead-final ( e.g. , Verb-final ) l ) hrase structure in Japanese .
|
P84-1007 |
However , no derivation of an affixed string generated by G2 * manifests any greater degree of center
embedding
; hence , the affixed strings associated with the expressions of L2 can still be assigned to them by a finite-state parser .
|
P84-1007 |
Moreover , under each interpretation , each of these sentences manifests first-degree center
embedding
.
|
C80-1009 |
As he points out , this is a simple and natural way of treating any construction capable of unlimited
embedding
.
|
J89-1005 |
The second-longest paper , " On the design of finite transducers for parsing phrase-structure languages " by Langendoen and Langsam , defines a finite transducer that recognizes a context-free fragment of English that contains left and right embedding and finite central
embedding
.
|
W11-1827 |
However , diverging from that system 's more pragmatic na - ture , we more clearly distinguish the shared task concerns from a general semantic composition scheme , that is based on the notion of
embedding
.
|
C82-2022 |
- I01 - 1.2 , Another problem is proposed by the structural ambi - 8 ~ ity of sequences of PP 's , for which both the "
embedding
" and the " same-level " hypotheses are presented !
|
C80-1074 |
They are classified into six groups , that is , modal ( Fl ) , nominalization ( FII ) ,
embedding
( fill ) , connecting ( FIV ) , elliptical ( F V ) and anaphoric operator ( Fvl ) .
|
W15-1504 |
The method , Instance-context
embedding
( ICE ) , leverages neural word embed - dings , and the correlation statistics they cap - ture , to compute high quality embeddings of word contexts .
|