Concordance

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a <term> trainable sentence planner </term>	for	a <term> spoken dialogue system </term> by	#2060 In this paper We experimentally evaluate a trainable sentence plannerfor a spoken dialogue system by eliciting subjective human judgments.
variables . This paper describes a method	for	<term> utterance classification </term> that	#2210 This paper describes a method for utterance classification that does not require manual transcription of training data.
to train a <term> phone n-gram model </term>	for	a particular <term> domain </term> ; the <term>	#2270 In our method, unsupervised training is first used to train a phone n-gram modelfor a particular domain; the output of recognition with this model is then passed to a phone-string classifier.
different <term> answering agents </term> searching	for	<term> answers </term> in multiple <term> corpora	#2346 Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora.
present <term> ONTOSCORE </term> , a system	for	scoring sets of <term> concepts </term> on	#2444 In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology.
</term> . Our empirical results , which hold	for	all examined <term> language pairs </term>	#2594 Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations.
</term> . The <term> model </term> is designed	for	use in <term> error correction </term> , with	#2716 The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.
systems </term> in order to make it more useful	for	<term> NLP tasks </term> . We present an implementation	#2740 The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.
stochastic disambiguation techniques </term>	for	<term> Lexical-Functional Grammars ( LFG	#2795 We present an application of ambiguity packing and stochastic disambiguation techniquesfor Lexical-Functional Grammars (LFG) to the domain of sentence condensation.
<term> linguistic parser/generator </term>	for	<term> LFG </term> , a <term> transfer component	#2814 Our system incorporates a linguistic parser/generatorfor LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection.
</term> , a <term> transfer component </term>	for	<term> parse reduction </term> operating on	#2820 Our system incorporates a linguistic parser/generator for LFG, a transfer componentfor parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection.
and a <term> maximum-entropy model </term>	for	<term> stochastic output selection </term>	#2833 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy modelfor stochastic output selection.
standard <term> parser evaluation methods </term>	for	automatically evaluating the <term> summarization	#2849 Furthermore, we propose the use of standard parser evaluation methodsfor automatically evaluating the summarization quality of sentence condensation systems.
Sources of <term> training data </term> suitable	for	<term> language modeling </term> of <term> conversational	#3019 Sources of training data suitable for language modeling of conversational speech are limited.
simple <term> unsupervised technique </term>	for	learning <term> morphology </term> by identifying	#3159 We describe a simple unsupervised techniquefor learning morphology by identifying hubs in an automaton.
present a <term> syntax-based constraint </term>	for	<term> word alignment </term> , known as the	#3233 We present a syntax-based constraintfor word alignment, known as the cohesion constraint.
that correspond to the <term> concept </term>	for	the targeted <term> NE </term> , e.g. he/she/man	#3321 This approach only requires a few common noun or pronoun seeds that correspond to the conceptfor the targeted NE, e.g. he/she/man/woman for PERSON NE.
<term> NE </term> , e.g. he/she/man / woman	for	<term> PERSON NE </term> . The <term> bootstrapping	#3330 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.
approaches <term> supervised NE </term> performance	for	some <term> NE types </term> . In this paper	#3384 The resulting NE system approaches supervised NE performance for some NE types.
a <term> phrase-based unigram model </term>	for	<term> statistical machine translation </term>	#3399 In this paper, we describe a phrase-based unigram modelfor statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models.


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