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other,25-2-C94-1030,bq	Japanese bunsetsu </term> and an <term> English	word	</term> , and to correct these <term> errors	#20667 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains.
tech,6-1-N03-2017,bq	<term> syntax-based constraint </term> for <term>	word	alignment </term> , known as the <term> cohesion	#3234 We present a syntax-based constraint forword alignment, known as the cohesion constraint.
model,14-4-N03-2036,bq	projections </term> using an underlying <term>	word	alignment </term> . We show experimental	#3458 During training, the blocks are learned from source interval projections using an underlyingword alignment.
tech,4-1-H05-1012,bq	This paper presents a <term> maximum entropy	word	alignment algorithm </term> for <term> Arabic-English	#7255 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data.
tech,6-2-C04-1192,bq	method exploits recent advances in <term>	word	alignment </term> and <term> word clustering	#6456 The method exploits recent advances inword alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus.
tech,14-2-P05-1034,bq	segmentation </term> and an <term> unsupervised	word	alignment component </term> . We align a <term>	#9240 This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component.
tech,4-4-H05-1012,bq	Significant improvement over traditional <term>	word	alignment techniques </term> is shown as	#7311 Significant improvement over traditionalword alignment techniques is shown as well as improvement on several machine translation tests.
other,12-5-P05-1074,bq	methods </term> using a set of <term> manual	word	alignments </term> , and contrast the <term>	#9764 We evaluate our paraphrase extractio and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.
measure(ment),16-3-H92-1016,bq	combined to reduce the <term> speech recognition	word	and sentence error rates </term> by a factor	#18755 Together with the use of a larger training set, these modifications combined to reduce the speech recognition word and sentence error rates by a factor of 2.5 and 1.6, respectively, on the October '91 test set.
other,8-10-H01-1042,bq	Additionally , they were asked to mark the <term>	word	</term> at which they made this decision	#747 Additionally, they were asked to mark theword at which they made this decision.
other,13-1-E06-1031,bq	high <term> costs </term> to movements of <term>	word	</term> blocks . In many cases though such	#10336 Most state-of-the-art evaluation measures for machine translation assign high costs to movements ofword blocks.
tech,10-6-C90-3072,bq	method has been developed for easy <term>	word	classification </term> . We describe the	#16862 Further, a special method has been developed for easyword classification.
tech,9-2-C04-1192,bq	advances in <term> word alignment </term> and <term>	word	clustering </term> based on <term> automatic	#6459 The method exploits recent advances in word alignment andword clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus.
other,11-1-P03-1051,bq	</term> by a <term> model </term> that a <term>	word	</term> consists of a sequence of <term> morphemes	#4611 We approximate Arabic's rich morphology by a model that aword consists of a sequence of morphemes in the pattern prefix-stem-suffix (* denotes zero or more occurrences of a morpheme).
tech,6-3-E06-1018,bq	approaches it utilizes <term> clustering of	word	co-occurrences </term> . This approach differs	#10133 Like most existing approaches it utilizes clustering of word co-occurrences.
other,4-8-E06-1031,bq	</term> . Results from experiments with <term>	word	dependent substitution costs </term> will	#10443 Results from experiments withword dependent substitution costs will demonstrate an additional increase of correlation between automatic evaluation measures and human judgment.
other,31-6-A94-1007,bq	analysis cost </term> , the improvement of <term>	word	disambiguation </term> , the interpretation	#19846 The model is based on a balance matching operation for two lists of the feature sets, which provides four effects: the reduction of analysis cost, the improvement ofword disambiguation, the interpretation of ellipses, and robust analysis.
measure(ment),20-3-N03-1018,bq	significantly reduce <term> character and	word	error rate </term> , and provide evaluation	#2766 We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
measure(ment),14-4-H90-1060,bq	recognition </term> , we achieved a 7.5 % <term>	word	error rate </term> on a standard <term> grammar	#17084 With only 12 training speakers for SI recognition, we achieved a 7.5%word error rate on a standard grammar and test set from the DARPA Resource Management corpus.
other,11-3-C04-1036,bq	empirical quality of <term> distributional	word	feature vectors </term> and its impact on	#5335 Motivated by this semantic criterion we analyze the empirical quality of distributional word feature vectors and its impact on word similarity results, proposing an objective measure for evaluating feature vector quality.


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