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	An alternative <term> index </term> could be the activity such as discussing , planning , informing , story-telling , etc . This paper addresses the problem of the <term> automatic detection </term> of those activities in meeting situation and everyday rejoinders .	#107 An alternative index could be the activity such as discussing, planning, informing, story-telling, etc. This paper addresses the problem of the automatic detection of those activities in meeting situation and everyday rejoinders.
	We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> . This , the first experiment in a series of experiments , looks at the <term> intelligibility </term> of <term> MT output </term> .	#609 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. This , the first experiment in a series of experiments, looks at the intelligibility of MT output.
	We describe our use of this approach in numerous fielded <term> user studies </term> conducted with the U.S. military . This paper proposes a practical approach employing <term> n-gram models </term> and <term> error-correction rules </term> for <term> Thai key prediction </term> and <term> Thai-English language identification </term> .	#1241 We describe our use of this approach in numerous fielded user studies conducted with the U.S. military. This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification.
	Our approach yields <term> phrasal and single word lexical paraphrases </term> as well as <term> syntactic paraphrases </term> . This paper presents a <term> formal analysis </term> for a large class of <term> words </term> called <term> alternative markers </term> , which includes <term> other ( than ) </term> , <term> such ( as ) </term> , and <term> besides </term> .	#1815 Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases. This paper presents a formal analysis for a large class of words called alternative markers, which includes other (than), such (as), and besides.
	We report on different aspects of the <term> predictive performance </term> of our <term> models </term> , including the influence of various <term> training and testing factors </term> on <term> predictive performance </term> , and examine the relationships among the target variables . This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> .	#2205 We report on different aspects of the predictive performance of our models, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables. This paper describes a method for utterance classification that does not require manual transcription of training data.
	A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging </term> using <term> concept-based seeds </term> and <term> successive learners </term> is presented . This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3305 A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE.
	The experimental results prove our claim that accurate <term> predicate-argument structures </term> enable high quality <term> IE </term> results . This paper proposes the <term> Hierarchical Directed Acyclic Graph ( HDAG ) Kernel </term> for <term> structured natural language data </term> .	#3789 The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results. This paper proposes the Hierarchical Directed Acyclic Graph (HDAG) Kernel for structured natural language data.
	Experimental results validate our hypothesis . This paper concerns the <term> discourse understanding process </term> in <term> spoken dialogue systems </term> .	#4126 Experimental results validate our hypothesis. This paper concerns the discourse understanding process in spoken dialogue systems.
	This paper concerns the <term> discourse understanding process </term> in <term> spoken dialogue systems </term> . This process enables the <term> system </term> to understand <term> user utterances </term> based on the <term> context </term> of a <term> dialogue </term> .	#4138 This paper concerns the discourse understanding process in spoken dialogue systems. This process enables the system to understand user utterances based on the context of a dialogue.
	By holding multiple <term> candidates </term> for <term> understanding </term> results and resolving the <term> ambiguity </term> as the <term> dialogue </term> progresses , the <term> discourse understanding accuracy </term> can be improved . This paper proposes a method for resolving this <term> ambiguity </term> based on <term> statistical information </term> obtained from <term> dialogue corpora </term> .	#4216 By holding multiple candidates for understanding results and resolving the ambiguity as the dialogue progresses, the discourse understanding accuracy can be improved. This paper proposes a method for resolving this ambiguity based on statistical information obtained from dialogue corpora.
	Experimental evaluation shows that the <term> cooperative responses </term> adaptive to <term> individual users </term> serve as good guidance for <term> novice users </term> without increasing the <term> dialogue duration </term> for <term> skilled users </term> . This paper presents an <term> unsupervised learning approach </term> to building a <term> non-English ( Arabic ) stemmer </term> .	#4430 Experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users. This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer.
	We also investigate the reason for that difference . This paper presents a <term> machine learning </term> approach to bare <term> sluice disambiguation </term> in <term> dialogue </term> .	#5149 We also investigate the reason for that difference. This paper presents a machine learning approach to bare sluice disambiguation in dialogue.
	We present a <term> text mining method </term> for finding <term> synonymous expressions </term> based on the <term> distributional hypothesis </term> in a set of coherent <term> corpora </term> . This paper proposes a new methodology to improve the <term> accuracy </term> of a <term> term aggregation system </term> using each author 's text as a coherent <term> corpus </term> .	#6114 We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherent corpus.
	We present <term> Minimum Bayes-Risk ( MBR ) decoding </term> for <term> statistical machine translation </term> . This statistical approach aims to minimize <term> expected loss of translation errors </term> under <term> loss functions </term> that measure <term> translation performance </term> .	#6556 We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance.
	We describe a new system that enhances <term> Criterion </term> 's capability , by evaluating multiple aspects of <term> coherence </term> in <term> essays </term> . This system identifies <term> features </term> of <term> sentences </term> based on <term> semantic similarity measures </term> and <term> discourse structure </term> .	#6690 We describe a new system that enhances Criterion's capability, by evaluating multiple aspects of coherence in essays. This system identifies features of sentences based on semantic similarity measures and discourse structure.
	We evaluated the <term> topic signatures </term> on a <term> WSD </term> task , where we trained a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term> , with promising results . This paper presents a novel <term> ensemble learning approach </term> to <term> resolving German pronouns </term> .	#7021 We evaluated the topic signatures on a WSD task, where we trained a second-order vector co-occurrence algorithm on standard WSD datasets, with promising results. This paper presents a novel ensemble learning approach to resolving German pronouns.
	We demonstrate how errors in the <term> machine translations </term> of the input <term> Arabic documents </term> can be corrected by identifying and generating from such <term> redundancy </term> , focusing on <term> noun phrases </term> . This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> .	#7249 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. This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data.
	<term> Performance </term> of the <term> algorithm </term> is contrasted with <term> human annotation performance </term> . This paper presents a <term> phrase-based statistical machine translation method </term> , based on <term> non-contiguous phrases </term> , i.e. <term> phrases </term> with gaps .	#7337 Performance of the algorithm is contrasted with human annotation performance. This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps.
	Experimental results are presented , that demonstrate how the proposed <term> method </term> allows to better generalize from the <term> training data </term> . This paper investigates some <term> computational problems </term> associated with <term> probabilistic translation models </term> that have recently been adopted in the literature on <term> machine translation </term> .	#7434 Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data. This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation.
	Yet , they are scarcely used for the assessment of <term> language pairs </term> like <term> English-Chinese </term> or <term> English-Japanese </term> , because of the <term> word segmentation problem </term> . This study establishes the equivalence between the standard use of <term> BLEU </term> in <term> word n-grams </term> and its application at the <term> character </term> level .	#7724 Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of the word segmentation problem. This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level.


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