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	to make use of <term> expectations </term> ,	based	both on knowledge of <term> surface English	#13038 Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described.
	within a <term> unification framework </term> ,	based	on <term> typed feature structures </term>	#20757 This paper describes the enhancements made, within a unification framework, based on typed feature structures, in order to support linking of lexical entries to their translation equivalents.
	</term> . In this paper , a new mechanism ,	based	on the concept of <term> sublanguage </term>	#18293 In this paper, a new mechanism, based on the concept of sublanguage, is proposed for identifying unknown words, especially personal names, in Chinese newspapers.
	statistical machine translation method </term> ,	based	on <term> non-contiguous phrases </term> ,	#7347 This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps.
	</term> for the <term> reranking task </term> ,	based	on the <term> boosting approach </term> to <term>	#8769 We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998).
	</term> from <term> structured data </term> (	based	on a <term> typing-algorithm </term> and <term>	#1983 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and a learning algorithm from structured data ( based on a typing-algorithm and type-unification).
	actually have worse coverage than accounts	based	on processing . Finally , it shows how	#21188 This paper reviews the theoretical literature, and shows why many of the theoretical accounts actually have worse coverage than accounts based on processing.
	built a <term> generation algorithm </term>	based	on the results . The evaluation using another	#5706 We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithmbased on the results.
	for resolving this <term> ambiguity </term>	based	on <term> statistical information </term> obtained	#4225 This paper proposes a method for resolving this ambiguitybased on statistical information obtained from dialogue corpora.
	algorithm </term> for <term> Arabic-English </term>	based	on <term> supervised training data </term>	#7260 This paper presents a maximum entropy word alignment algorithm for Arabic-Englishbased on supervised training data.
tech,8-3-P06-2012,bq	</term> show that this <term> spectral clustering	based	approach </term> outperforms the other <term>	#11384 Experiment results on ACE corpora show that this spectral clustering based approach outperforms the other clustering methods.
	alignment </term> and <term> word clustering </term>	based	on <term> automatic extraction of translation	#6461 The method exploits recent advances in word alignment and word clusteringbased on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus.
	on <term> block selection criteria </term>	based	on <term> unigram </term> counts and <term> phrase	#3469 We show experimental results on block selection criteriabased on unigram counts and phrase length.
	for <term> word sense disambiguation </term>	based	on <term> parallel corpora </term> . The method	#6445 The paper presents a method for word sense disambiguationbased on parallel corpora.
	translation quality </term> of <term> EBMT </term>	based	on a small-sized <term> bilingual corpus </term>	#3088 In order to boost the translation quality of EBMTbased on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus.
tech,7-10-I05-2048,bq	coupled with <term> rule-based and example	based	machine translation modules </term> to build	#8178 It has also successfully been coupled with rule-based and example based machine translation modules to build a multi engine machine translation system.
	finding <term> synonymous expressions </term>	based	on the <term> distributional hypothesis </term>	#6102 We present a text mining method for finding synonymous expressionsbased on the distributional hypothesis in a set of coherent corpora.
	) as well as <term> binary features </term>	based	on the <term> block </term> identities themselves	#9614 We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary featuresbased on the block identities themselves, e.g. block bigram features.
	We show that various <term> features </term>	based	on the structure of <term> email-threads </term>	#6287 We show that various featuresbased on the structure of email-threads can be used to improve upon lexical similarity of discourse segments for question-answer pairing.
	of <term> phrase boundary heuristics </term>	based	on the placement of <term> function words	#17675 This is facilitated through the use of phrase boundary heuristicsbased on the placement of function words, and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words.


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