| tech,4-1-P05-1058,ak | This paper proposes an <term> alignment adaptation approach </term> to improve <term> domain-specific ( in-domain ) word alignment </term> . | #9687 This paper proposes analignment adaptation approach to improve domain-specific (in-domain) word alignment. | |
| tech,9-1-P05-1058,ak | This paper proposes an <term> alignment adaptation approach </term> to improve <term> domain-specific ( in-domain ) word alignment </term> . | #9692 This paper proposes an alignment adaptation approach to improvedomain-specific ( in-domain ) word alignment. | |
| tech,4-2-P05-1058,ak | The basic idea of <term> alignment adaptation </term> is to use <term> out-of-domain corpus </term> to improve <term> in-domain word alignment results </term> . | #9703 The basic idea ofalignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. | |
| lr,9-2-P05-1058,ak | The basic idea of <term> alignment adaptation </term> is to use <term> out-of-domain corpus </term> to improve <term> in-domain word alignment results </term> . | #9708 The basic idea of alignment adaptation is to useout-of-domain corpus to improve in-domain word alignment results. | |
| other,13-2-P05-1058,ak | The basic idea of <term> alignment adaptation </term> is to use <term> out-of-domain corpus </term> to improve <term> in-domain word alignment results </term> . | #9712 The basic idea of alignment adaptation is to use out-of-domain corpus to improvein-domain word alignment results. | |
| model,8-3-P05-1058,ak | In this paper , we first train two <term> statistical word alignment models </term> with the <term> large-scale out-of-domain corpus </term> and the <term> small-scale in-domain corpus </term> respectively , and then interpolate these two <term> models </term> to improve the <term> domain-specific word alignment </term> . | #9725 In this paper, we first train twostatistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. | |
| lr,14-3-P05-1058,ak | In this paper , we first train two <term> statistical word alignment models </term> with the <term> large-scale out-of-domain corpus </term> and the <term> small-scale in-domain corpus </term> respectively , and then interpolate these two <term> models </term> to improve the <term> domain-specific word alignment </term> . | #9731 In this paper, we first train two statistical word alignment models with thelarge-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. | |
| lr,19-3-P05-1058,ak | In this paper , we first train two <term> statistical word alignment models </term> with the <term> large-scale out-of-domain corpus </term> and the <term> small-scale in-domain corpus </term> respectively , and then interpolate these two <term> models </term> to improve the <term> domain-specific word alignment </term> . | #9736 In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and thesmall-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. | |
| model,29-3-P05-1058,ak | In this paper , we first train two <term> statistical word alignment models </term> with the <term> large-scale out-of-domain corpus </term> and the <term> small-scale in-domain corpus </term> respectively , and then interpolate these two <term> models </term> to improve the <term> domain-specific word alignment </term> . | #9746 In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these twomodels to improve the domain-specific word alignment. | |
| tech,33-3-P05-1058,ak | In this paper , we first train two <term> statistical word alignment models </term> with the <term> large-scale out-of-domain corpus </term> and the <term> small-scale in-domain corpus </term> respectively , and then interpolate these two <term> models </term> to improve the <term> domain-specific word alignment </term> . | #9750 In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve thedomain-specific word alignment. | |
| tech,7-4-P05-1058,ak | Experimental results show that our approach improves <term> domain-specific word alignment </term> in terms of both <term> precision </term> and <term> recall </term> , achieving a <term> relative error rate reduction </term> of 6.56 % as compared with the state-of-the-art technologies . | #9761 Experimental results show that our approach improvesdomain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies. | |
| measure(ment),14-4-P05-1058,ak | Experimental results show that our approach improves <term> domain-specific word alignment </term> in terms of both <term> precision </term> and <term> recall </term> , achieving a <term> relative error rate reduction </term> of 6.56 % as compared with the state-of-the-art technologies . | #9768 Experimental results show that our approach improves domain-specific word alignment in terms of bothprecision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies. | |
| measure(ment),16-4-P05-1058,ak | Experimental results show that our approach improves <term> domain-specific word alignment </term> in terms of both <term> precision </term> and <term> recall </term> , achieving a <term> relative error rate reduction </term> of 6.56 % as compared with the state-of-the-art technologies . | #9770 Experimental results show that our approach improves domain-specific word alignment in terms of both precision andrecall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies. | |
| measure(ment),20-4-P05-1058,ak | Experimental results show that our approach improves <term> domain-specific word alignment </term> in terms of both <term> precision </term> and <term> recall </term> , achieving a <term> relative error rate reduction </term> of 6.56 % as compared with the state-of-the-art technologies . | #9774 Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving arelative error rate reduction of 6.56% as compared with the state-of-the-art technologies. |
