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tech,18-2-H92-1060,bq	</term> already in place for the full <term>	linguistic	analysis component </term> . Robust <term>	#19382 This robust parsing capability was achieved through minor extensions of pre-existing components already in place for the fulllinguistic analysis component.
tech,27-1-H92-1060,bq	a <term> question </term> when a full <term>	linguistic	analysis </term> fails . This <term> robust	#19360 This paper describes an extension to the MIT ATIS (Air Travel Information Service) system, which allows it to answer a question when a fulllinguistic analysis fails.
other,6-2-C86-1132,bq	RAREAS </term> draws on several kinds of <term>	linguistic	and non-linguistic knowledge </term> and	#13952 RAREAS draws on several kinds oflinguistic and non-linguistic knowledge and mirrors a forecaster's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events.
other,5-3-A94-1011,bq	</term> . A novel method for adding <term>	linguistic	annotation </term> to <term> corpora </term>	#19950 A novel method for addinglinguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit.
other,16-8-C88-2162,bq	hierarchy </term> is used in predicting new <term>	linguistic	concepts </term> . Thus , a <term> program </term>	#15884 Second, we show in this paper how a lexical hierarchy is used in predicting newlinguistic concepts.
other,3-7-C88-2162,bq	identified two tasks : First , how <term>	linguistic	concepts </term> are acquired from <term> training	#15844 First, howlinguistic concepts are acquired from training examples and organized in a hierarchy; this task was discussed in previous papers [Zernik87].
lr,30-3-J90-3002,bq	ensure the validity of such complex <term>	linguistic	databases </term> . Our most important task	#17290 If we want valuable lexicons and grammars to achieve complex natural language processing, we must provide very powerful tools to help create and ensure the validity of such complexlinguistic databases.
other,17-2-C88-2162,bq	does not readily lend itself in the <term>	linguistic	domain </term> . For another , <term> linguistic	#15767 For one thing, learning methodology applicable in general domains does not readily lend itself in thelinguistic domain.
other,8-3-P86-1011,bq	turn to a discussion comparing the <term>	linguistic	expressiveness </term> of the two <term> formalisms	#14616 We then turn to a discussion comparing thelinguistic expressiveness of the two formalisms.
other,7-2-P01-1070,bq	</term> , which are built from <term> shallow	linguistic	features </term> of <term> questions </term>	#2151 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals.
	power of <term> phrasal SMT </term> with the	linguistic	generality available in a <term> parser </term>	#9304 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
other,27-1-C04-1112,bq	classification ( maximum entropy ) </term> with <term>	linguistic	information </term> . Instead of building	#6006 In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) withlinguistic information.
other,12-3-N04-1022,bq	that incorporate different levels of <term>	linguistic	information </term> from <term> word strings	#6587 We describe a hierarchy of loss functions that incorporate different levels oflinguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences.
other,20-1-H92-1026,bq	, that takes advantage of detailed <term>	linguistic	information </term> to resolve <term> ambiguity	#18913 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailedlinguistic information to resolve ambiguity.
other,15-4-H92-1026,bq	grammar </term> tailoring via the usual <term>	linguistic	introspection </term> in the hope of generating	#18996 This stands in contrast to the usual approach of further grammar tailoring via the usuallinguistic introspection in the hope of generating the correct parse.
other,11-1-P86-1038,bq	of <term> features </term> to describe <term>	linguistic	objects </term> . Although <term> computational	#14634 Unification-based grammar formalisms use structures containing sets of features to describelinguistic objects.
tech,4-2-N03-1026,bq	Our <term> system </term> incorporates a <term>	linguistic	parser/generator </term> for <term> LFG </term>	#2812 Our system incorporates alinguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection.
other,12-3-P06-2059,bq	certain <term> layout structures </term> and <term>	linguistic	pattern </term> . By using them , we can	#11438 The idea behind our method is to utilize certain layout structures andlinguistic pattern.
other,4-4-C88-2162,bq	learning </term> . We introduced a new <term>	linguistic	representation </term> , the <term> Dynamic	#15790 We introduced a newlinguistic representation, the Dynamic Hierarchical Phrasal Lexicon (DHPL) [Zernik88], to facilitate language acquisition.
other,3-3-C88-2162,bq	linguistic domain </term> . For another , <term>	linguistic	representation </term> used by <term> language	#15773 For another,linguistic representation used by language processing systems is not geared to learning.


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