Concordance

Query C88-2162 22 (1,025.7 per million)

tech,9-1-C88-2162,bq	Computer programs so far have not fared well in <term> modeling language acquisition </term> .	#15746 Computer programs so far have not fared well inmodeling language acquisition.
other,4-2-C88-2162,bq	For one thing , <term> learning methodology </term> applicable in <term> general domains </term> does not readily lend itself in the <term> linguistic domain </term> .	#15754 For one thing,learning methodology applicable in general domains does not readily lend itself in the linguistic domain.
other,8-2-C88-2162,bq	For one thing , <term> learning methodology </term> applicable in <term> general domains </term> does not readily lend itself in the <term> linguistic domain </term> .	#15758 For one thing, learning methodology applicable ingeneral domains does not readily lend itself in the linguistic domain.
other,17-2-C88-2162,bq	For one thing , <term> learning methodology </term> applicable in <term> general domains </term> does not readily lend itself in the <term> linguistic domain </term> .	#15767 For one thing, learning methodology applicable in general domains does not readily lend itself in thelinguistic domain.
other,3-3-C88-2162,bq	For another , <term> linguistic representation </term> used by <term> language processing systems </term> is not geared to <term> learning </term> .	#15773 For another,linguistic representation used by language processing systems is not geared to learning.
tech,7-3-C88-2162,bq	For another , <term> linguistic representation </term> used by <term> language processing systems </term> is not geared to <term> learning </term> .	#15777 For another, linguistic representation used bylanguage processing systems is not geared to learning.
other,14-3-C88-2162,bq	For another , <term> linguistic representation </term> used by <term> language processing systems </term> is not geared to <term> learning </term> .	#15784 For another, linguistic representation used by language processing systems is not geared tolearning.
other,4-4-C88-2162,bq	We introduced a new <term> linguistic representation </term> , the <term> Dynamic Hierarchical Phrasal Lexicon ( DHPL ) </term> [ Zernik88 ] , to facilitate <term> language acquisition </term> .	#15790 We introduced a newlinguistic representation, the Dynamic Hierarchical Phrasal Lexicon (DHPL) [Zernik88], to facilitate language acquisition.
other,8-4-C88-2162,bq	We introduced a new <term> linguistic representation </term> , the <term> Dynamic Hierarchical Phrasal Lexicon ( DHPL ) </term> [ Zernik88 ] , to facilitate <term> language acquisition </term> .	#15794 We introduced a new linguistic representation, theDynamic Hierarchical Phrasal Lexicon ( DHPL ) [Zernik88], to facilitate language acquisition.
tech,21-4-C88-2162,bq	We introduced a new <term> linguistic representation </term> , the <term> Dynamic Hierarchical Phrasal Lexicon ( DHPL ) </term> [ Zernik88 ] , to facilitate <term> language acquisition </term> .	#15807 We introduced a new linguistic representation, the Dynamic Hierarchical Phrasal Lexicon (DHPL) [Zernik88], to facilitatelanguage acquisition.
other,4-5-C88-2162,bq	From this , a <term> language learning model </term> was implemented in the program <term> RINA </term> , which enhances its own <term> lexical hierarchy </term> by processing examples in context .	#15814 From this, alanguage learning model was implemented in the program RINA, which enhances its own lexical hierarchy by processing examples in context.
tool,12-5-C88-2162,bq	From this , a <term> language learning model </term> was implemented in the program <term> RINA </term> , which enhances its own <term> lexical hierarchy </term> by processing examples in context .	#15822 From this, a language learning model was implemented in the programRINA, which enhances its own lexical hierarchy by processing examples in context.
other,18-5-C88-2162,bq	From this , a <term> language learning model </term> was implemented in the program <term> RINA </term> , which enhances its own <term> lexical hierarchy </term> by processing examples in context .	#15828 From this, a language learning model was implemented in the program RINA, which enhances its ownlexical hierarchy by processing examples in context.
other,3-7-C88-2162,bq	First , how <term> linguistic concepts </term> are acquired from <term> training examples </term> and organized in a <term> hierarchy </term> ; this task was discussed in previous papers [ Zernik87 ] .	#15844 First, howlinguistic concepts are acquired from training examples and organized in a hierarchy; this task was discussed in previous papers [Zernik87].
tool,8-7-C88-2162,bq	First , how <term> linguistic concepts </term> are acquired from <term> training examples </term> and organized in a <term> hierarchy </term> ; this task was discussed in previous papers [ Zernik87 ] .	#15849 First, how linguistic concepts are acquired fromtraining examples and organized in a hierarchy; this task was discussed in previous papers [Zernik87].
other,14-7-C88-2162,bq	First , how <term> linguistic concepts </term> are acquired from <term> training examples </term> and organized in a <term> hierarchy </term> ; this task was discussed in previous papers [ Zernik87 ] .	#15855 First, how linguistic concepts are acquired from training examples and organized in ahierarchy; this task was discussed in previous papers [Zernik87].
other,9-8-C88-2162,bq	Second , we show in this paper how a <term> lexical hierarchy </term> is used in predicting new <term> linguistic concepts </term> .	#15877 Second, we show in this paper how alexical hierarchy is used in predicting new linguistic concepts.
other,16-8-C88-2162,bq	Second , we show in this paper how a <term> lexical hierarchy </term> is used in predicting new <term> linguistic concepts </term> .	#15884 Second, we show in this paper how a lexical hierarchy is used in predicting newlinguistic concepts.
other,3-9-C88-2162,bq	Thus , a <term> program </term> does not stall even in the presence of a <term> lexical unknown </term> , and a <term> hypothesis </term> can be produced for covering that <term> lexical gap </term> .	#15890 Thus, aprogram does not stall even in the presence of a lexical unknown, and a hypothesis can be produced for covering that lexical gap.
other,13-9-C88-2162,bq	Thus , a <term> program </term> does not stall even in the presence of a <term> lexical unknown </term> , and a <term> hypothesis </term> can be produced for covering that <term> lexical gap </term> .	#15900 Thus, a program does not stall even in the presence of alexical unknown, and a hypothesis can be produced for covering that lexical gap.


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