C80-1074 Let fII and fIII be the nominalization and the embedding /embedding/NN operator respectively .
A00-2015 This paper proposes a statistical method for learning dependency preference of Japanese subordinate clauses , in which scope embedding /embedding/NN preference of subordinate clauses is exploited as a useful information source for disambiguating dependencies between subordinate clauses .
D12-1086 Our best model based on Euclidean co-occurrence embedding /embedding/NN combines the paradigmatic context representation with morphological and orthographic features and achieves 80 % many-to-one accuracy on a 45-tag 1M word corpus .
C90-2071 How - ever , this " Maximal Conventionality Principle " can easily be overruled by global considerations arising from the embedding /embedding/NN phrase and context .
C80-1074 Then , the modal operator ( Fill , FII 2 and FI21 ) , embedding /embedding/NN operator fill and connecting operator FIV are extracted by investigating the variety and the inflectional form of the predicate or the words which follow the predicate .
C92-3137 In turn , simulative reasoning call produce resuits app \ -RSB- icable to the attitude context where it takes place , and may sometimes affect related contexts , e.g. , causing re-Evaluation of the attitude in the embedding /embedding/NN attitude contexts .
C92-2095 ( = S ) on tile right and so eliminate unnecessary center - embedding /embedding/NN ; 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 .
C80-1074 They are classified into six groups , that is , modal ( Fl ) , nominalization ( FII ) , embedding /embedding/NN ( fill ) , connecting ( FIV ) , elliptical ( F V ) and anaphoric operator ( Fvl ) .
C92-2072 Note that these ` errors ' are not syntactically incorrect , but are constructions which , if overused , may result in poor writing , and as such are often included in style-checker ` hit-lists ' ; thus , we would include multiple embedding /embedding/NN constructions , poten - ACT , S DE COLING-92 , NANTES , 23-28 AOIJT 1992 4 6 8 PROC .
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 /embedding/NN " and the " same-level " hypotheses are presented !
D14-1012 In this study , we investigate and analyze three different approaches , including a new proposed distributional prototype approach , for utilizing the embedding /embedding/NN features .
D14-1012 Experiments on the task of named entity recognition show that each of the proposed approaches can better utilize the word embedding /embedding/NN features , among which the distributional prototype approach performs the best .
D14-1012 Moreover , the combination of the approaches provides additive im - provements , outperforming the dense and continuous embedding /embedding/NN features by nearly 2 points of F1 score .
D14-1015 We investigate how to improve bilingual embedding /embedding/NN which has been successfully used as a feature in phrase-based statistical machine translation ( SMT ) .
D14-1015 Despite bilingual embedding /embedding/NN 's success , the contextual information , which is of critical importance to translation quality , was ignored in previous work .
D14-1015 To employ the contextual information , we propose a simple and memory-efficient model for learning bilingual embedding /embedding/NN , taking both the source phrase and context around the phrase into account .
D14-1015 Bilingual translation scores generated from our proposed bilingual embedding /embedding/NN model are used as features in our SMT system .
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 /embedding/NN ) , and ( 3 ) compute output probabilities for incoming words given the context .
D14-1030 Sec - ondly , the neural network embedding /embedding/NN of word i can predict the MEG activity when word i is presented to the subject , revealing that it is correlated with the brain 's own representation of word i. Moreover , we obtain that the activity is predicted in different regions of the brain with varying delay .
D14-1062 By embedding /embed/VBG our latent domain phrase model in a sentence-level model and training the two in tandem , we are able to adapt all core translation components together -- phrase , lexical and reordering .
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