Concordance

Query with 120 (5,594.7 per million)

	</term> ( a <term> verb final language </term>	with	<term> overt case markers </term> , relatively	#460 The key features of the system include: (i) Robust efficient parsing of Korean (a verb final languagewith overt case markers, relatively free word order, and frequent omissions of arguments).
	. The results of this experiment , along	with	a preliminary analysis of the factors involved	#762 The results of this experiment, along with a preliminary analysis of the factors involved in the decision making process will be presented here.
tech,9-1-H01-1049,bq	new paradigm for <term> human interaction	with	data sources </term> . We integrate a <term>	#791 Listen-Communicate-Show (LCS) is a new paradigm for human interaction with data sources.
	spoken language understanding system </term>	with	<term> intelligent mobile agents </term> that	#802 We integrate a spoken language understanding systemwith intelligent mobile agents that mediate between users and information sources.
	</term> , tactical personnel can converse	with	their logistics system to place a supply	#835 Using LCS-Marine, tactical personnel can converse with their logistics system to place a supply or information request.
	this capability in several field exercises	with	the Marines and are currently developing	#896 We have demonstrated this capability in several field exercises with the Marines and are currently developing applications of this technology in new domains.
	</term> and selects the <term> word string </term>	with	the best <term> performance </term> ( typically	#1082 The oracle knows the reference word string and selects the word stringwith 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.
	acts like a <term> dynamic combiner </term>	with	<term> hard decisions </term> using the <term>	#1124 Actually, the oracle acts like a dynamic combinerwith hard decisions using the reference.
	method amounts to tagging <term> LMs </term>	with	<term> confidence measures </term> and picking	#1178 The method amounts to tagging LMswith confidence measures and picking the best hypothesis corresponding to the LM with the best confidence.
	</term> corresponding to the <term> LM </term>	with	the best <term> confidence </term> . We describe	#1190 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LMwith the best confidence.
	fielded <term> user studies </term> conducted	with	the U.S. military . This paper proposes	#1236 We describe our use of this approach in numerous fielded user studies conducted with the U.S. military.
	word-segmented data </term> , in combination	with	a range of <term> local segment contiguity	#1515 We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character- and word-segmented data, in combination with a range of local segment contiguity models (in the form of N-grams).
	formal analysis </term> that is compatible	with	the <term> search engine </term> 's <term> operational	#1895 I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics.
	</term> of <term> utterances </term> produced	with	<term> trainable components </term> can compete	#2038 Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches.
	<term> trainable components </term> can compete	with	<term> hand-crafted template-based or rule-based	#2043 Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches.
	domain independent acoustic models </term>	with	off-the-shelf <term> classifiers </term> to	#2230 The method combines domain independent acoustic modelswith 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.
	<term> output </term> of <term> recognition </term>	with	this <term> model </term> is then passed to	#2279 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognitionwith this model is then passed to a phone-string classifier.
	for use in <term> error correction </term> ,	with	a focus on <term> post-processing </term> the	#2722 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.
	<term> system </term> is state-of-the-art ,	with	guaranteed <term> grammaticality </term> of	#2893 Overall summarization quality of the proposed system is state-of-the-art, with guaranteed grammaticality of the system output due to the use of a constraint-based parser/generator.
	<term> training data </term> can be supplemented	with	<term> text </term> from the <term> web </term>	#3040 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.


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