D151191 
proposes contextdependent KG

embedding

, a twostage scheme that takes

J801001 
grammars have a straightforward

embedding

, but which permit various transformations

W153820 
representations , also known as word

embedding

, have been shown to improve

J003002 
stank . Discussing such center

embedding

, Johnson ( 1998 ) presents the

C001055 
functions such as the level of

embedding

, number of parentheses , and

W144002 
aspects of the direct context ( an

embedding

, parent phrase pair ) of the

J003002 
the unacceptability of center

embedding

, preference for lower attach

P132087 
that takes as input an existing

embedding

, some labeled data , and produces

D141015 
memoryefficient model for learning bilingual

embedding

, taking both the source phrase

W154310 
representation . Besides word

embedding

, we use partofspeech ( POS

W104104 
other examples of this section are

embedding

, while this example is of overlapping

C922095 
eliminate unnecessary center 

embedding

; and ( 3 ) eliminating of scrambling

P841007 
manifests any greater degree of center

embedding

; hence , the affixed strings

P841007 
manifests firstdegree center

embedding

. In E3 , the included VP saw

C801009 
construction capable of unlimited

embedding

. The results of this treatment

J891005 
right embedding and finite central

embedding

. They argue that " the theory

W111827 
that is based on the notion of

embedding

. We apply our methodology to

C822022 
of PP 's , for which both the "

embedding

" and the " samelevel " hypotheses

C801074 
Fl ) , nominalization ( FII ) ,

embedding

( fill ) , connecting ( FIV )

W151504 
The method , Instancecontext

embedding

( ICE ) , leverages neural word
