Concordance

Query P05-1074 19 >
Sort Node 19 (885.8 per million)

tech,1-3-P05-1074,bq	available <term> resource </term> . Using <term>	alignment techniques	</term> from <term> phrase-based statistical	#9688 Usingalignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot.
other,24-5-P05-1074,bq	<term> paraphrases </term> extracted from <term>	automatic alignments	</term> . We present a <term> Czech-English	#9775 We evaluate our paraphrase extractio and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted fromautomatic alignments.
lr,9-2-P05-1074,bq	show that this task can be done using <term>	bilingual parallel corpora	</term> , a much more commonly available <term>	#9676 We show that this task can be done usingbilingual parallel corpora, a much more commonly available resource.
lr,11-4-P05-1074,bq	paraphrases </term> extracted from a <term>	bilingual parallel corpus	</term> to be ranked using <term> translation	#9727 We define a paraphrase probability that allows paraphrases extracted from abilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account.
other,30-4-P05-1074,bq	show how it can be refined to take <term>	contextual information	</term> into account . We evaluate our <term>	#9746 We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to takecontextual information into account.
other,15-3-P05-1074,bq	how <term> paraphrases </term> in one <term>	language	</term> can be identified using a <term> phrase	#9702 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in onelanguage can be identified using a phrase in another language as a pivot.
other,12-5-P05-1074,bq	ranking methods </term> using a set of <term>	manual word alignments	</term> , and contrast the <term> quality </term>	#9763 We evaluate our paraphrase extractio and ranking methods using a set ofmanual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.
lr,4-1-P05-1074,bq	task </term> . Previous work has used <term>	monolingual parallel corpora	</term> to extract and generate <term> paraphrases	#9658 Previous work has usedmonolingual parallel corpora to extract and generate paraphrases.
tech,3-5-P05-1074,bq	</term> into account . We evaluate our <term>	paraphrase extractio and ranking methods	</term> using a set of <term> manual word alignments	#9754 We evaluate ourparaphrase extractio and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.
other,3-4-P05-1074,bq	language as a pivot . We define a <term>	paraphrase probability	</term> that allows <term> paraphrases </term>	#9719 We define aparaphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account.
other,11-1-P05-1074,bq	corpora </term> to extract and generate <term>	paraphrases	</term> . We show that this task can be done	#9665 Previous work has used monolingual parallel corpora to extract and generateparaphrases.
other,12-3-P05-1074,bq	machine translation </term> , we show how <term>	paraphrases	</term> in one <term> language </term> can be	#9699 Using alignment techniques from phrase-based statistical machine translation, we show howparaphrases in one language can be identified using a phrase in another language as a pivot.
other,7-4-P05-1074,bq	paraphrase probability </term> that allows <term>	paraphrases	</term> extracted from a <term> bilingual parallel	#9723 We define a paraphrase probability that allowsparaphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account.
other,21-5-P05-1074,bq	contrast the <term> quality </term> with <term>	paraphrases	</term> extracted from <term> automatic alignments	#9772 We evaluate our paraphrase extractio and ranking methods using a set of manual word alignments, and contrast the quality withparaphrases extracted from automatic alignments.
other,21-3-P05-1074,bq	language </term> can be identified using a <term>	phrase	</term> in another language as a pivot .	#9708 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using aphrase in another language as a pivot.
tech,4-3-P05-1074,bq	<term> alignment techniques </term> from <term>	phrase-based statistical machine translation	</term> , we show how <term> paraphrases </term>	#9691 Using alignment techniques fromphrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot.
measure(ment),19-5-P05-1074,bq	alignments </term> , and contrast the <term>	quality	</term> with <term> paraphrases </term> extracted	#9770 We evaluate our paraphrase extractio and ranking methods using a set of manual word alignments, and contrast thequality with paraphrases extracted from automatic alignments.
lr,18-2-P05-1074,bq	</term> , a much more commonly available <term>	resource	</term> . Using <term> alignment techniques	#9685 We show that this task can be done using bilingual parallel corpora, a much more commonly availableresource.
other,18-4-P05-1074,bq	parallel corpus </term> to be ranked using <term>	translation probabilities	</term> , and show how it can be refined	#9734 We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked usingtranslation probabilities, and show how it can be refined to take contextual information into account.


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