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<term> Communicator </term> participants are	using	. In this presentation , we describe the	#251 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using.
elicited from duplicating the experiment	using	<term> machine translation output </term> .	#677 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output.
<term> word string </term> has been obtained by	using	a different <term> LM </term> . Actually ,	#1110 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.
combiner </term> with <term> hard decisions </term>	using	the <term> reference </term> . We provide experimental	#1127 Actually, the oracle acts like a dynamic combiner with hard decisionsusing the reference.
mimics the behavior of the <term> oracle </term>	using	a <term> neural network </term> or a <term> decision	#1163 We suggest a method that mimics the behavior of the oracleusing a neural network or a decision tree.
surprisingly close to what can be achieved	using	conventional <term> word-trigram recognition	#2247 The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription.
performance gains from the <term> data </term> by	using	<term> class-dependent interpolation </term>	#3073 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.
NIST score </term> demonstrated the effect of	using	an out-of-domain <term> bilingual corpus </term>	#3139 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model.
bilingual corpus </term> and the possibility of	using	the <term> language model </term> . We describe	#3148 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model.
<term> Named Entity ( NE ) tagging </term>	using	<term> concept-based seeds </term> and <term>	#3296 A novel bootstrapping approach to Named Entity (NE) taggingusing concept-based seeds and successive learners is presented.
<term> source interval projections </term>	using	an underlying <term> word alignment </term>	#3455 During training, the blocks are learned from source interval projectionsusing an underlying word alignment.
subcategorization frame ( SCF ) </term> distributions	using	the <term> Information Bottleneck </term> and	#3916 We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods.
derived by <term> decision tree learning </term>	using	real <term> dialogue data </term> collected	#4362 Moreover, the models are automatically derived by decision tree learningusing real dialogue data collected by the system.
proprietary <term> Arabic stemmer </term> built	using	<term> rules </term> , <term> affix lists </term>	#4551 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component.
</term> . <term> Task-based evaluation </term>	using	<term> Arabic information retrieval </term>	#4571 Task-based evaluationusing Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
</term> based on the results . The evaluation	using	another 23 subjects showed that the proposed	#5713 The evaluation using another 23 subjects showed that the proposed method could effectively generate proper referring expressions.
of a <term> term aggregation system </term>	using	each author 's text as a coherent <term>	#6129 This paper proposes a new methodology to improve the accuracy of a term aggregation systemusing each author's text as a coherent corpus.
</term> in <term> unannotated text </term> by	using	a fully automatic sequence of <term> preprocessing	#7083 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process.
patterns </term> in <term> translation data </term>	using	<term> part-of-speech tag sequences </term>	#7639 We describe a method for identifying systematic patterns in translation datausing part-of-speech tag sequences.
sense disambiguation performance </term> ,	using	standard <term> WSD evaluation methodology	#7814 We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task.


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