| ability to spend their time finding more | data | relevant to their task , and gives them | #3615 It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology. | |
| </term> to recover a <term> submanifold </term> of | data | from a <term> high dimensionality space </term> | #11358 It works by calculating eigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. | |
| Information System ) domain </term> . This | data | collection effort has been co-ordinated | #18545 This data collection effort has been co-ordinated by MADCOW (Multi-site ATIS Data COllection Working group). | |
| Understanding System ) </term> , which creates the | data | for a <term> text retrieval application </term> | #21409 Our document understanding technology is implemented in a system called IDUS (Intelligent Document Understanding System), which creates the data for a text retrieval application and the automatic generation of hypertext links. | |
| lr,11-2-P03-1058,bq | automatically acquire <term> sense-tagged training | data | </term> from <term> English-Chinese parallel | #4834 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. |
| lr,13-1-H05-1012,bq | </term> based on <term> supervised training | data | </term> . We demonstrate that it is feasible | #7264 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data. |
| lr,13-4-P03-1033,bq | learning </term> using real <term> dialogue | data | </term> collected by the <term> system </term> | #4365 Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. |
| lr,14-1-P03-1058,bq | is the lack of <term> manually sense-tagged | data | </term> required for <term> supervised learning | #4815 A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. |
| lr,15-1-N03-1001,bq | manual transcription </term> of <term> training | data | </term> . The method combines <term> domain | #2221 This paper describes a method for utterance classification that does not require manual transcription of training data. |
| lr,17-5-H05-1095,bq | better generalize from the <term> training | data | </term> . This paper investigates some <term> | #7432 Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data. |
| lr,19-2-P01-1004,bq | both <term> character - and word-segmented | data | </term> , in combination with a range of <term> | #1511 We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character- and word-segmented data, in combination with a range of local segment contiguity models (in the form of N-grams). |
| lr,2-1-N03-2003,bq | </term> result . Sources of <term> training | data | </term> suitable for <term> language modeling | #3017 Sources of training data suitable for language modeling of conversational speech are limited. |
| lr,27-3-H90-1060,bq | the usual pooling of all the <term> speech | data | </term> from many <term> speakers </term> prior | #17062 In addition, combination of the training speakers is done by averaging the statistics> of independently trained models rather than the usual pooling of all the speech data from many speakers prior to training. |
| lr,37-4-P03-1058,bq | advantage that <term> manually sense-tagged | data | </term> have in their <term> sense coverage | #4908 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. |
| lr,43-2-N03-2003,bq | bigger performance gains from the <term> | data | </term> by using <term> class-dependent interpolation | #3071 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from thedata by using class-dependent interpolation of N-grams. |
| lr,6-3-J05-4003,bq | approach </term> , we extract <term> parallel | data | </term> from large <term> Chinese , Arabic | #9032 Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. |
| lr,7-2-N03-2003,bq | In this paper , we show how <term> training | data | </term> can be supplemented with <term> text | #3036 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. |
| lr,7-4-N03-1012,bq | <term> system </term> against the <term> annotated | data | </term> shows that , it successfully classifies | #2509 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%). |
| lr,8-6-N01-1003,bq | automatically learned from <term> training | data | </term> . We show that the trained <term> SPR | #1431 The SPR uses ranking rules automatically learned from training data. |
| lr,9-1-H05-2007,bq | <term> patterns </term> in <term> translation | data | </term> using <term> part-of-speech tag sequences | #7638 We describe a method for identifying systematic patterns in translation data using part-of-speech tag sequences. |
