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tech,2-3-J05-4003,bq	Using this <term> approach </term> , we extract <term> parallel data </term> from large <term> Chinese , Arabic , and English non-parallel newspaper corpora </term> .	#9027 Using thisapproach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora.
lr,10-3-J05-4003,bq	Using this <term> approach </term> , we extract <term> parallel data </term> from large <term> Chinese , Arabic , and English non-parallel newspaper corpora </term> .	#9035 Using this approach, we extract parallel data from largeChinese , Arabic , and English non-parallel newspaper corpora.
lr,10-1-J05-4003,bq	We present a novel <term> method </term> for <term> discovering parallel sentences </term> in <term> comparable , non-parallel corpora </term> .	#8993 We present a novel method for discovering parallel sentences incomparable , non-parallel corpora.
tech,6-1-J05-4003,bq	We present a novel <term> method </term> for <term> discovering parallel sentences </term> in <term> comparable , non-parallel corpora </term> .	#8989 We present a novel method fordiscovering parallel sentences in comparable, non-parallel corpora.
other,11-6-J05-4003,bq	Thus , our method can be applied with great benefit to <term> language pairs </term> for which only scarce <term> resources </term> are available .	#9111 Thus, our method can be applied with great benefit tolanguage pairs for which only scarce resources are available.
tech,3-2-J05-4003,bq	We train a <term> maximum entropy classifier </term> that , given a pair of <term> sentences </term> , can reliably determine whether or not they are <term> translations </term> of each other .	#9001 We train amaximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other.
tech,4-1-J05-4003,bq	We present a novel <term> method </term> for <term> discovering parallel sentences </term> in <term> comparable , non-parallel corpora </term> .	#8987 We present a novelmethod for discovering parallel sentences in comparable, non-parallel corpora.
tech,6-5-J05-4003,bq	We also show that a good-quality <term> MT system </term> can be built from scratch by starting with a very small <term> parallel corpus </term> ( 100,000 <term> words </term> ) and exploiting a large <term> non-parallel corpus </term> .	#9074 We also show that a good-qualityMT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus.
lr,29-5-J05-4003,bq	We also show that a good-quality <term> MT system </term> can be built from scratch by starting with a very small <term> parallel corpus </term> ( 100,000 <term> words </term> ) and exploiting a large <term> non-parallel corpus </term> .	#9097 We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a largenon-parallel corpus.
lr,19-5-J05-4003,bq	We also show that a good-quality <term> MT system </term> can be built from scratch by starting with a very small <term> parallel corpus </term> ( 100,000 <term> words </term> ) and exploiting a large <term> non-parallel corpus </term> .	#9087 We also show that a good-quality MT system can be built from scratch by starting with a very smallparallel corpus (100,000 words) and exploiting a large non-parallel corpus.
lr,6-3-J05-4003,bq	Using this <term> approach </term> , we extract <term> parallel data </term> from large <term> Chinese , Arabic , and English non-parallel newspaper corpora </term> .	#9031 Using this approach, we extractparallel data from large Chinese, Arabic, and English non-parallel newspaper corpora.
measure(ment),3-4-J05-4003,bq	We evaluate the <term> quality of the extracted data </term> by showing that it improves the performance of a state-of-the-art <term> statistical machine translation system </term> .	#9048 We evaluate thequality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system.
lr,17-6-J05-4003,bq	Thus , our method can be applied with great benefit to <term> language pairs </term> for which only scarce <term> resources </term> are available .	#9117 Thus, our method can be applied with great benefit to language pairs for which only scarceresources are available.
other,12-2-J05-4003,bq	We train a <term> maximum entropy classifier </term> that , given a pair of <term> sentences </term> , can reliably determine whether or not they are <term> translations </term> of each other .	#9010 We train a maximum entropy classifier that, given a pair ofsentences, can reliably determine whether or not they are translations of each other.
tech,18-4-J05-4003,bq	We evaluate the <term> quality of the extracted data </term> by showing that it improves the performance of a state-of-the-art <term> statistical machine translation system </term> .	#9063 We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-artstatistical machine translation system.
other,22-2-J05-4003,bq	We train a <term> maximum entropy classifier </term> that , given a pair of <term> sentences </term> , can reliably determine whether or not they are <term> translations </term> of each other .	#9020 We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they aretranslations of each other.
other,23-5-J05-4003,bq	We also show that a good-quality <term> MT system </term> can be built from scratch by starting with a very small <term> parallel corpus </term> ( 100,000 <term> words </term> ) and exploiting a large <term> non-parallel corpus </term> .	#9091 We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000words) and exploiting a large non-parallel corpus.


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