| tech,9-1-H01-1049,ak | paradigm for <term> human interaction with | data | sources </term> . We integrate a <term> spoken | #792 Listen-Communicate-Show (LCS) is a new paradigm for human interaction with data sources. | |
| lr,8-6-N01-1003,ak | 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. | |
| over both character - and word-segmented | data | , in combination with a range of <term> local | #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). | ||
| other,34-2-P01-1047,ak | learning algorithm </term> from <term> structured | data | </term> ( based on a <term> typing-algorithm | #1982 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and a learning algorithm from structured data (based on a typing-algorithm and type-unification). | |
| lr,15-1-N03-1001,ak | manual transcription </term> of <term> training | data | </term> . The method combines <term> domain | #2222 This paper describes a method for utterance classification that does not require manual transcription of training data. | |
| lr,7-4-N03-1012,ak | <term> system </term> against the <term> annotated | data | </term> shows that , it successfully classifies | #2510 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,2-1-N03-2003,ak | result </term> . Sources of <term> training | data | </term> suitable for <term> language modeling | #3018 Sources of training data suitable for language modeling of conversational speech are limited. | |
| lr,7-2-N03-2003,ak | In this paper , we show how <term> training | data | </term> can be supplemented with <term> text | #3037 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,43-2-N03-2003,ak | bigger performance gains from the <term> | data | </term> by using <term> class-dependent interpolation | #3072 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. | |
| large inflow of multilingual , multimedia | data | . It gives users the ability to spend their | #3603 The TAP-XL Automated Analyst's Assistant is an application designed to help an English-speaking analyst write a topical report, culling information from a large inflow of multilingual, multimedia data. | ||
| ability to spend their time finding more | data | relevant to their task , and gives them | #3616 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. | ||
| other,15-3-N03-4010,ak | browsing the <term> repository </term> of <term> | data | objects </term> created by the <term> system | #3700 The operation of the system will be explained in depth through browsing the repository ofdata objects created by the system during each question answering session. | |
| other,13-1-P03-1005,ak | </term> for <term> structured natural language | data | </term> . The <term> HDAG Kernel </term> directly | #3806 This paper proposes the Hierarchical Directed Acyclic Graph (HDAG) Kernel for structured natural language data. | |
| other,15-1-P03-1009,ak | classes </term> from undisambiguated <term> corpus | data | </term> . We describe a new approach which | #3901 Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. | |
| other,31-4-P03-1009,ak | semantically classifying undisambiguated <term> SCF | data | </term> . We apply a <term> decision tree based | #3972 A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data. | |
| lr,13-4-P03-1033,ak | learning </term> using real <term> dialogue | data | </term> collected by the <term> system </term> | #4367 Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. | |
| lr,14-1-P03-1058,ak | is the lack of <term> manually sense-tagged | data | </term> required for <term> supervised learning | #4817 A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. | |
| lr,11-2-P03-1058,ak | automatically acquire <term> sense-tagged training | data | </term> from <term> English-Chinese parallel | #4836 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,8-3-P03-1058,ak | this method of acquiring <term> sense-tagged | data | </term> is promising . On a subset of the | #4867 Our investigation reveals that this method of acquiring sense-tagged data is promising. | |
| lr,37-4-P03-1058,ak | advantage that <term> manually sense-tagged | data | </term> have in their sense coverage . Our | #4910 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. |
