| 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. | |
| lr,30-3-J90-3002,bq | ensure the validity of such complex <term> | linguistic | databases </term> . Our most important task | #17290 If we want valuable lexicons and grammars to achieve complex natural language processing, we must provide very powerful tools to help create and ensure the validity of such complexlinguistic databases. |
| 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,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,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. |
| 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,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,15-4-H92-1026,bq | grammar </term> tailoring via the usual <term> | linguistic | introspection </term> in the hope of generating | #18996 This stands in contrast to the usual approach of further grammar tailoring via the usuallinguistic introspection in the hope of generating the correct parse. |
| 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. |
| 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,20-1-H92-1026,bq | , that takes advantage of detailed <term> | linguistic | information </term> to resolve <term> ambiguity | #18913 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailedlinguistic information to resolve ambiguity. |
| 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,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,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,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. |
| 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,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,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,5-3-A94-1011,bq | </term> . A novel method for adding <term> | linguistic | annotation </term> to <term> corpora </term> | #19950 A novel method for addinglinguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit. |
| 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. |
