Concordance

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retrieval accuracy </term> , but much faster . We	also	provide evidence that our findings are	#1588 We also provide evidence that our findings are scalable.
can make a fair copy of not only texts but	also	graphs and tables indispensable to our	#12256 In this paper, we report a system FROFF which can make a fair copy of not only texts but also graphs and tables indispensable to our papers.
extensive system development effort but	also	improves the <term> transliteration accuracy	#5835 Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the transliteration accuracy significantly.
research </term> . This piece of work has	also	laid a foundation for exploring and harvesting	#8298 This piece of work has also laid a foundation for exploring and harvesting English-Chinese bitexts in a larger volume from the Web.
evaluate their relative performances . We	also	introduce a new strategy , called <term>	#10841 We also introduce a new strategy, called Begin/After tagging or BIA, and show that it is competitive to the best other strategies.
way . This <term> generation system </term>	also	uses <term> disjunctive feature structures	#16249 This generation systemalso uses disjunctive feature structures to reduce the number of copies of the derivation tree.
model ’s score </term> of 88.2 % . The article	also	introduces a new <term> algorithm </term> for	#8863 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data.
interface </term> for browsing and editing was	also	designed and implemented . The principle	#17330 A customized interface for browsing and editing was also designed and implemented.
</term> of <term> abstract moves </term> . We	also	present a prototype <term> concordancer </term>	#11760 We also present a prototype concordancer, CARE, which exploits the move-tagged abstracts for digital learning.
appear cooperative or graceful unless they	also	incorporate numerous <term> non-literal aspects	#12569 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.
view of <term> language definition </term> are	also	noted . Representative samples from an <term>	#13401 Several advantages from the point of view of language definition are also noted.
field of <term> speech processing </term> , but	also	in the related areas of <term> Human-Machine	#18636 The paper provides an overview of the research conducted at LIMSI in the field of speech processing, but also in the related areas of Human-Machine Communication, including Natural Language Processing, Non Verbal and Multimodal Communication.
machine translation task </term> , which can	also	be viewed as a <term> stochastic tree-to-tree	#9489 Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer.
database </term> . <term> Requestors </term> can	also	instruct the <term> system </term> to notify	#866 Requestors can also instruct the system to notify them when the status of a request changes or when a request is complete.
carries important information yet it is	also	time consuming to document . Given the	#11 Oral communication is ubiquitous and carries important information yet it is also time consuming to document.
algorithm </term> . In addition , it could	also	be used to help evaluate <term> disambiguation	#19315 In addition, it could also be used to help evaluate disambiguation algorithms that did not make use of the discourse constraint.
96 % and a <term> recall </term> of 98 % . It	also	gets a <term> precision </term> of 70 % and	#11262 It also gets a precision of 70% and a recall of 49% in the task of placing commas.
statistical machine translation system </term> . We	also	show that a good-quality <term> MT system	#9069 We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus.
vital to <term> machine translation </term> are	also	discussed together with various interesting	#13314 Some examples of the difference between Japanese sentence structure and English sentence structure, which is vital to machine translation are also discussed together with various interesting ambiguities.
set </term> is 49.3 % . Similar results were	also	obtained on the <term> February '92 benchmark	#18798 Similar results were also obtained on the February '92 benchmark evaluation.


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