Concordance

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statistical machine translation tool kit </term> ,	will	be introduced and used to build a working	#8129 STTK, a statistical machine translation tool kit, will be introduced and used to build a working translation system.
of these systems , <term> accuracy </term>	will	always be imperfect . For many reasons	#6782 Despite the successes of these systems, accuracywill always be imperfect.
information sources </term> . We have built and	will	demonstrate an application of this approach	#818 We have built and will demonstrate an application of this approach called LCS-Marine.
word dependent substitution costs </term>	will	demonstrate an additional increase of correlation	#10447 Results from experiments with word dependent substitution costswill demonstrate an additional increase of correlation between automatic evaluation measures and human judgment.
<term> free text </term> . The demonstration	will	focus on how <term> JAVELIN </term> processes	#3664 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus.
<term> natural language interfaces </term>	will	never appear cooperative or graceful unless	#12561 While such decoding is an essential underpinning, much recent work suggests that natural language interfaceswill never appear cooperative or graceful unless they also incorporate numerous non-literal aspects of communication, such as robust communication procedures.
source code </term> of the <term> tool kit </term>	will	be made available . In this paper we present	#8198 The source code of the tool kitwill be made available.
target word selection </term> . This paper	will	concentrate on the second requirement .	#20261 This paper will concentrate on the second requirement.
involved in the decision making process	will	be presented here . <term> Listen-Communicate-Show	#775 The results of this experiment, along with a preliminary analysis of the factors involved in the decision making process will be presented here.
in the <term> sentence </term> , the process	will	extend to both the left and the right of	#15584 So, for any place where the easily identifiable fragments occur in the sentence, the process will extend to both the left and the right of the islands, until possibly completely missing fragments are reached.
<term> genre </term> . Examples and results	will	be given for <term> Arabic </term> , but the	#4513 Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal.
language pairs </term> . The experimental results	will	show that it significantly outperforms	#10427 The experimental results will show that it significantly outperforms state-of-the-art approaches in sentence-level correlation.
The operation of the <term> system </term>	will	be explained in depth through browsing	#3689 The operation of the systemwill be explained in depth through browsing the repository of data objects created by the system during each question answering session.
these <term> evaluation techniques </term>	will	provide information about both the <term>	#587 We believe that these evaluation techniqueswill provide information about both the human language learning process, the translation process and the development of machine translation systems.
assumption that the input <term> text </term>	will	be in reasonably neat form , e.g. , <term>	#12958 Most large text-understanding systems have been designed under the assumption that the input textwill be in reasonably neat form, e.g., newspaper stories and other edited texts.
it is actually possible , and after that	will	lead to predictions of missing <term> fragments	#15559 We shall introduce the concept of a chart that works outward from islands and makes sense of as much of the sentence as it is actually possible, and after that will lead to predictions of missing fragments.
aspects of a <term> parse tree </term> that	will	determine the correct <term> parse </term>	#18972 We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence.
cover the basics of <term> SMT </term> : Theory	will	be put into practice . <term> STTK </term>	#8114 Theory will be put into practice.
</term> , it is extremely likely that they	will	all share the same <term> sense </term> . This	#19253 That is, if a polysemous word such 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> users </term> of our <term> tool </term>	will	drive a <term> syntax-based decoder </term>	#9914 In our demonstration at ACL, new users of our toolwill drive a syntax-based decoder for themselves.


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