Concordance

Query word 73 >
Shuffle 73 (3,403.4 per million)

other,4-3-H01-1058,bq	<term> oracle </term> knows the <term> reference	word	string </term> and selects the <term> word	#1075 The oracle knows the reference word string and selects the word string with 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.
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.
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.
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.
other,19-4-C04-1036,bq	<term> feature vectors </term> and better <term>	word	similarity </term> performance . The work	#5375 Finally, a novel feature weighting and selection function is presented, which yields superior feature vectors and betterword similarity performance.
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,20-5-P03-1051,bq	<term> stems </term> from a 155 million <term>	word	</term><term> unsegmented corpus </term> ,	#4726 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 millionword unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
other,20-2-I05-2014,bq	English-Japanese </term> , because of the <term>	word	segmentation problem </term> . This study	#7720 Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of theword segmentation problem.
tech,7-1-C04-1112,bq	we present a <term> corpus-based supervised	word	sense disambiguation ( WSD ) system </term>	#5988 In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) with linguistic information.
tech,22-6-E06-1018,bq	) idea of <term> evaluation </term> of <term>	word	sense disambiguation algorithms </term> is	#10205 Additionally, a novel and likewise automatic and unsupervised evaluation method inspired by Schutze's (1992) idea of evaluation ofword sense disambiguation algorithms is employed.
tech,27-3-A94-1017,bq	structural disambiguation </term> and <term> target	word	selection </term> . This paper will concentrate	#20256 We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation and target word selection.
measure(ment),23-1-I05-2021,bq	Chinese-to-English SMT model </term> directly on <term>	word	sense disambiguation performance </term>	#7809 We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly onword sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task.
other,19-6-P03-1051,bq	test corpus </term> containing 28,449 <term>	word	tokens </term> . We believe this is a state-of-the-art	#4762 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens.
tech,12-4-H01-1041,bq	disambiguation </term> and accurate <term>	word	order generation </term> of the <term> target	#489 (ii) High quality translation via word sense disambiguation and accurateword order generation of the target language.
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.
other,18-3-P06-2110,bq	<term> LSA-based and the cooccurrence-based	word	vectors </term> better reflect <term> associative	#11558 The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity.
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,2-6-P03-1051,bq	corpus </term> . The resulting <term> Arabic	word	segmentation system </term> achieves around	#4746 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens.
other,6-2-P04-2005,bq	particular <term> concept </term> , or <term>	word	sense </term> , a <term> topic signature </term>	#6911 Given a particular concept, orword sense, a topic signature is a set of words that tend to co-occur with it.
tech,10-2-I05-2021,bq	designing and evaluating dedicated <term>	word	sense disambiguation ( WSD ) models </term>	#7839 Much effort has been put in designing and evaluating dedicatedword sense disambiguation (WSD) models, in particular with the Senseval series of workshops.


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