| other,7-2-P01-1070,bq | </term> , which are built from <term> shallow | linguistic | features </term> of <term> questions </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> | #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 | classification ( maximum entropy ) </term> with <term> | linguistic | information </term> . Instead of building | #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 | that incorporate different levels of <term> | linguistic | information </term> from <term> word strings | #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. |
| 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 | certain <term> layout structures </term> and <term> | linguistic | pattern </term> . By using them , we can | #11438 The idea behind our method is to utilize certain layout structures andlinguistic pattern. |
| other,6-2-C86-1132,bq | RAREAS </term> draws on several kinds of <term> | linguistic | and non-linguistic knowledge </term> and | #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 | <term> utterances </term> ( called the <term> | linguistic | structure </term> ) , a structure of <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 | <term> attentional state </term> ) . The <term> | linguistic | structure </term> consists of segments of | #14157 Thelinguistic structure consists of segments of the discourse into which the utterances naturally aggregate. |
| other,13-4-J86-3001,bq | purposes </term> , expressed in each of the <term> | linguistic | segments </term> as well as relationships | #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 | turn to a discussion comparing the <term> | linguistic | expressiveness </term> of the two <term> formalisms | #14616 We then turn to a discussion comparing thelinguistic expressiveness of the two formalisms. |
| other,11-1-P86-1038,bq | of <term> features </term> to describe <term> | linguistic | objects </term> . Although <term> computational | #14634 Unification-based grammar formalisms use structures containing sets of features to describelinguistic objects. |
| other,21-2-C88-2160,bq | use any concepts of the underlying <term> | linguistic | theory </term> : it is a reformulation of | #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 | does not readily lend itself in the <term> | linguistic | domain </term> . For another , <term> linguistic | #15767 For one thing, learning methodology applicable in general domains does not readily lend itself in thelinguistic domain. |
| other,3-3-C88-2162,bq | linguistic domain </term> . For another , <term> | linguistic | representation </term> used by <term> language | #15773 For another,linguistic representation used by language processing systems is not geared to learning. |
| other,4-4-C88-2162,bq | learning </term> . We introduced a new <term> | linguistic | representation </term> , the <term> Dynamic | #15790 We introduced a newlinguistic representation, the Dynamic Hierarchical Phrasal Lexicon (DHPL) [Zernik88], to facilitate language acquisition. |
| other,3-7-C88-2162,bq | identified two tasks : First , how <term> | linguistic | concepts </term> are acquired from <term> training | #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 | hierarchy </term> is used in predicting new <term> | linguistic | concepts </term> . Thus , a <term> program </term> | #15884 Second, we show in this paper how a lexical hierarchy is used in predicting newlinguistic concepts. |
| tech,12-4-C90-3063,bq | statistics </term> on the output of other <term> | linguistic | tools </term> . An experiment was performed | #16667 The scheme was implemented by gathering statistics on the output of otherlinguistic tools. |
| other,27-2-J90-3002,bq | dictionary </term> on the basis of a <term> | linguistic | theory </term> . If we want valuable <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. |
