Concordance

Query linguistic 26 (1,212.2 per million)

other,7-2-P01-1070,bq	These <term> models </term> , which are built from <term> shallow linguistic features </term> of <term> questions </term> , are employed to predict target variables which represent a <term> user 's informational goals </term> .	#2151 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals.
tech,4-2-N03-1026,bq	Our <term> system </term> incorporates a <term> linguistic parser/generator </term> for <term> LFG </term> , a <term> transfer component </term> for <term> parse reduction </term> operating on <term> packed parse forests </term> , and a <term> maximum-entropy model </term> for <term> stochastic output selection </term> .	#2812 Our system incorporates alinguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection.
other,27-1-C04-1112,bq	In this paper , we present a <term> corpus-based supervised word sense disambiguation ( WSD ) system </term> for <term> Dutch </term> which combines <term> statistical classification ( maximum entropy ) </term> with <term> linguistic information </term> .	#6006 In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) withlinguistic information.
other,12-3-N04-1022,bq	We describe a hierarchy of <term> loss functions </term> that incorporate different levels of <term> linguistic information </term> from <term> word strings </term> , <term> word-to-word alignments </term> from an <term> MT system </term> , and <term> syntactic structure </term> from <term> parse-trees </term> of <term> source and target language sentences </term> .	#6587 We describe a hierarchy of loss functions that incorporate different levels oflinguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences.
	We describe an efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination with conventional <term> SMT models </term> provides a promising approach that incorporates the power of <term> phrasal SMT </term> with the linguistic generality available in a <term> parser </term> .	#9304 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
other,12-3-P06-2059,bq	The idea behind our method is to utilize certain <term> layout structures </term> and <term> linguistic pattern </term> .	#11438 The idea behind our method is to utilize certain layout structures andlinguistic pattern.
other,6-2-C86-1132,bq	<term> RAREAS </term> draws on several kinds of <term> linguistic and non-linguistic knowledge </term> and mirrors a forecaster 's apparent tendency to ascribe less precise <term> temporal adverbs </term> to more remote meteorological events .	#13952 RAREAS draws on several kinds oflinguistic and non-linguistic knowledge and mirrors a forecaster's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events.
other,25-2-J86-3001,bq	In this theory , <term> discourse structure </term> is composed of three separate but interrelated components : the structure of the sequence of <term> utterances </term> ( called the <term> linguistic structure </term> ) , a structure of <term> purposes </term> ( called the <term> intentional structure </term> ) , and the state of <term> focus of attention </term> ( called the <term> attentional state </term> ) .	#14127 In this theory, discourse structure is composed of three separate but interrelated components: the structure of the sequence of utterances (called thelinguistic structure), a structure of purposes (called the intentional structure), and the state of focus of attention (called the attentional state).
other,1-3-J86-3001,bq	The <term> linguistic structure </term> consists of segments of the <term> discourse </term> into which the <term> utterances </term> naturally aggregate .	#14157 Thelinguistic structure consists of segments of the discourse into which the utterances naturally aggregate.
other,13-4-J86-3001,bq	The <term> intentional structure </term> captures the <term> discourse-relevant purposes </term> , expressed in each of the <term> linguistic segments </term> as well as relationships among them .	#14185 The intentional structure captures the discourse-relevant purposes, expressed in each of thelinguistic segments as well as relationships among them.
other,8-3-P86-1011,bq	We then turn to a discussion comparing the <term> linguistic expressiveness </term> of the two <term> formalisms </term> .	#14616 We then turn to a discussion comparing thelinguistic expressiveness of the two formalisms.
other,11-1-P86-1038,bq	<term> Unification-based grammar formalisms </term> use structures containing sets of <term> features </term> to describe <term> linguistic objects </term> .	#14634 Unification-based grammar formalisms use structures containing sets of features to describelinguistic objects.
other,21-2-C88-2160,bq	The explanation of an <term> ambiguity </term> or an error for the purposes of correction does not use any concepts of the underlying <term> linguistic theory </term> : it is a reformulation of the erroneous or ambiguous <term> sentence </term> .	#15682 The explanation of an ambiguity or an error for the purposes of correction does not use any concepts of the underlyinglinguistic theory: it is a reformulation of the erroneous or ambiguous sentence.
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.
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,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].
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.
tech,12-4-C90-3063,bq	The scheme was implemented by gathering <term> statistics </term> on the output of other <term> linguistic tools </term> .	#16667 The scheme was implemented by gathering statistics on the output of otherlinguistic tools.
other,27-2-J90-3002,bq	The basic goal in building that <term> editor </term> was to provide an adequate tool to help <term> lexicologists </term> produce a valid and coherent <term> dictionary </term> on the basis of a <term> linguistic theory </term> .	#17257 The basic goal in building that editor was to provide an adequate tool to help lexicologists produce a valid and coherent dictionary on the basis of alinguistic theory.


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