| may offer additional <term> indices </term> | such | as the time and place of the rejoinder | #75 Traditional information retrieval techniques use a histogram of keywords as the document representation but oral communication may offer additional indicessuch as the time and place of the rejoinder and the attendance. | ||
| alternative <term> index </term> could be the activity | such | as discussing , planning , informing , | #95 An alternative index could be the activity such as discussing, planning, informing, story-telling, etc. | ||
| . Emotions and other <term> indices </term> | such | as the dominance distribution of speakers | #167 Emotions and other indicessuch as the dominance distribution of speakers might be available on the surface and could be used directly. | ||
| </term> , which includes other ( than ) , | such | ( as ) , and besides . These <term> words | #1839 This paper presents a formal analysis for a large class of words called alternative markers, which includes other (than), such (as), and besides. | ||
| corrected by identifying and generating from | such | <term> redundancy </term> , focusing on <term> | #5267 We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases. | ||
| </term> with gaps . A method for producing | such | <term> phrases </term> from a <term> word-aligned | #5605 A method for producing such phrases from a word-aligned corpora is proposed. | ||
| model </term> is also presented that deals | such | <term> phrases </term> , as well as a <term> | #5623 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. | ||
| to assess the correctness of answers to | such | questions involves manual determination | #5954 Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information nugget appears in a system's response. | ||
| Machine Translation ( MT ) systems </term> , | such | as <term> BLEU </term> or <term> NIST </term> , | #6232 Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU or NIST, are now well established. | ||
| certain field structured extraction tasks , | such | as classified advertisements and bibliographic | #9045 We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. | ||
| demonstrate how <term> semantic information </term> | such | as <term> WordNet </term> and <term> Name List | #9359 We also demonstrate how semantic informationsuch as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. | ||
| first introduce our approach to inducing | such | a <term> grammar </term> from <term> parallel | #9847 We first introduce our approach to inducing such a grammar from parallel corpora. | ||
| word blocks </term> . In many cases though | such | <term> movements </term> still result in correct | #11280 In many cases though such movements still result in correct or almost correct sentences. | ||
| and ( 3 ) <term> conversational cues </term> , | such | as <term> cue phrases </term> and <term> overlapping | #11528 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. | ||
| </term> : ( a ) numeric-valued attributes , | such | as <term> size </term> or <term> location </term> | #11603 This paper discusses two problems that arise in the Generation of Referring Expressions: (a) numeric-valued attributes, such as size or location; (b) perspective-taking in reference. | ||
| </term> . Finding the preferred language for | such | a <term> need </term> is a valuable task . | #11691 Finding the preferred language for such a need is a valuable task. | ||
| simulate many <term> grammar formalisms </term> , | such | as <term> rewriting systems </term> , <term> | #12041 This formalism is both elementary and powerful enough to strongly simulate many grammar formalisms, such as rewriting systems, dependency grammars, TAG, HPSG and LFG. | ||
| using them , we can automatically extract | such | <term> sentences </term> that express <term> | #12386 By using them, we can automatically extract such sentences that express opinion. | ||
| dialog model </term> . The development of | such | a <term> model </term> appears to be important | #13297 The development of such a model appears to be important in several respects: | ||
| </term> of input from their users . While | such | <term> decoding </term> is an essential underpinning | #13483 While such decoding is an essential underpinning, much recent work suggests that natural language interfaces will never appear cooperative or graceful unless they also incorporate numerous non-literal aspects of communication, such as robust communication procedures. |
