| 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 </term> and other <term> indices </term> | such | as the <term> 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. | |
| other,23-1-P01-1009,bq | includes <term> other ( than ) </term> , <term> | such | ( as ) </term> , and <term> besides </term> . | #1838 This paper presents a formal analysis for a large class of words called alternative markers, which includes other (than),such (as), and besides. |
| We then use the <term> predicates </term> of | such | <term> clauses </term> to create a set of <term> | #5188 We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. | |
| </term> between <term> objects </term> . However , | such | an approach does not work well when there | #5633 However, such an approach does not work well when there is no distinctive attribute among objects. | |
| collect <term> referring expressions </term> in | such | situations , and built a <term> generation | #5698 We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithm based on the results. | |
| from <term> unstructured data sources </term> , | such | as the <term> Web </term> or <term> newswire | #6766 Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents. | |
| Processing ( NLP ) </term> applications , | such | as <term> Word Sense Disambiguation ( WSD | #6946 Topic signatures can be useful in a number of Natural Language Processing (NLP) applications, such as Word Sense Disambiguation (WSD) and Text Summarisation. | |
| corrected by identifying and generating from | such | <term> redundancy </term> , focusing on <term> | #7241 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. | |
| gaps . A <term> method </term> for producing | such | <term> phrases </term> from a <term> word-aligned | #7361 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> | #7379 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 | #7553 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> , | #7689 Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU or NIST, are now well established. | |
| introduce our <term> approach </term> to inducing | such | a <term> grammar </term> from <term> parallel | #9467 We first introduce our approach to inducing such a grammar from parallel corpora. | |
| word </term> blocks . In many cases though | such | movements still result in correct or almost | #10343 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 | #10591 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> numeric-valued attributes </term> , | such | as size or location ; ( b ) <term> perspective-taking | #10666 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. | |
| Finding the preferred <term> language </term> for | such | a <term> need </term> is a valuable task . | #10754 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> | #11104 This formalism is both elementary and powerful enough to strongly simulate many grammar formalisms, such as rewriting systems, dependency grammars, TAG, HPSG and LFG. |
