Concordance

Query such 41 >
Sort Right 41 (1,911.5 per million)

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.
	</term> . We propose a method of attaining	such	a design through a method of <term> structure-sharing	#17984 We propose a method of attaining such a design through a method of structure-sharing which avoids log(d) overheads often associated with structure-sharing of graphs without any use of costly dependency pointers.
	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.
	<term> dialog model </term> . The development of	such	a <term> model </term> appears to be important	#12360 The development of such a model appears to be important in several respects:
	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.
	of <term> parsing flexibilities </term> that	such	a system should provide . We go , on to	#12750 In this paper, we outline a set of parsing flexibilities that such a system should provide.
	defeasible reasoning </term> , and presents	such	a treatment for <term> Japanese sentence	#16572 This paper proposes that sentence analysis should be treated as defeasible reasoning, and presents such a treatment for Japanese sentence analyses using an argumentation system by Konolige, which is a formalization of defeasible reasoning, that includes arguments and defeat rules that capture defeasibility.
	</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.
	including <term> coordinate conjunctions </term>	such	as <term> and </term> , <term> or </term> , <term>	#19687 The authors propose a model for analyzing English sentences including coordinate conjunctionssuch as and, or, but and the equivalent words.
	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.
	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> highly inflective languages </term>	such	as <term> Czech </term> , <term> Russian </term>	#16776 This approach is sufficient for languages with little inflection such as English, but fails for highly inflective languagessuch as Czech, Russian, Slovak or other Slavonic languages.
	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.
	speech </term> . Other contextual clues ,	such	as <term> editing terms </term> , <term> word	#21338 Other contextual clues, such as editing terms, word fragments, and word matchings, are also factored in by modifying the transition probabilities.
	languages with little <term> inflection </term>	such	as <term> English </term> , but fails for <term>	#16766 This approach is sufficient for languages with little inflectionsuch as English, but fails for highly inflective languages such as Czech, Russian, Slovak or other Slavonic languages.
	</term> , posing special problems for readers ,	such	as <term> misspelled words </term> , <term> missing	#13009 However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc.
	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.
	non-literal aspects of communication </term> ,	such	as robust <term> communication procedures	#12577 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.
	That is , if a <term> polysemous word </term>	such	as <term> sentence </term> appears two or more	#19234 That is, if a polysemous wordsuch as sentence appears two or more times in a well-written discourse, it is extremely likely that they will all share the same sense.
	) <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.


	in Help