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 .
|
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 .
|
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 .
|
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 .
|
C80-1009 |
As he points out , this is a simple and natural way of treating any construction capable of unlimited
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 ) .
|
J98-4006 |
Chomsky 's argument that finite-state devices are not able to represent natural language structures , especially those involving central
embedding
( recursion ) , was one of the reasons for this fact .
|
S14-2033 |
Coooolll is built in a supervised learning framework by concatenating the sentiment-specific word
embedding
( SSWE ) features with the state-of-the-art hand-crafted features .
|
D14-1030 |
Many statistical models for natural language processing exist , including context-based neural networks that ( 1 ) model the previously seen context as a latent feature vector , ( 2 ) integrate successive words into the context using some learned representation (
embedding
) , and ( 3 ) compute output probabilities for incoming words given the context .
|
D15-1031 |
We study the problem of jointly
embedding
a knowledge base and a text corpus .
|
J82-3001 |
The intuitive notion of "
embedding
a linguistic theory into a model of language use " as it is generally construed is much stronger than this , since it implies that the parsing system follows some ( perhaps all ) of the same operating principles as the linguistic system , and makes reference in its operation to the same system of rules .
|
T78-1035 |
Linguists have long recognized the desirability of
embedding
a theory of grammar within a theory of linguistic performance ( scc , e.g. , Chomsky ( 1965 ; 10-15 ) ) .
|
P15-2098 |
We show that radical
embedding
achieves com - parable , and sometimes even better , results than competing methods .
|
D15-1306 |
In quantitative analysis , we show that lexical and syntactic features are useful for automatic categorization of annoying behav - iors , and frame-semantic features further boost the performance ; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model ; and incorporating frame-semantic
embedding
achieves the best overall performance .
|