tech,9-1-H01-1049,bq <term> Listen-Communicate-Show ( LCS ) </term> is a new paradigm for <term> human interaction with data sources </term> .
lr,8-6-N01-1003,bq The <term> SPR </term> uses <term> ranking rules </term> automatically learned from <term> training data </term> .
lr,19-2-P01-1004,bq We take a selection of both <term> bag-of-words and segment order-sensitive string comparison methods </term> , and run each over both <term> character - and word-segmented data </term> , in combination with a range of <term> local segment contiguity models </term> ( in the form of <term> N-grams </term> ) .
other,34-2-P01-1047,bq Our <term> logical definition </term> leads to a neat relation to <term> categorial grammar </term> , ( yielding a treatment of <term> Montague semantics </term> ) , a <term> parsing-as-deduction </term> in a <term> resource sensitive logic </term> , and a <term> learning algorithm </term> from <term> structured data </term> ( based on a <term> typing-algorithm </term> and <term> type-unification </term> ) .
lr,15-1-N03-1001,bq This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> .
lr,7-4-N03-1012,bq An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) .
lr,2-1-N03-2003,bq Sources of <term> training data </term> suitable for <term> language modeling </term> of <term> conversational speech </term> are limited .
lr,7-2-N03-2003,bq In this paper , we show how <term> training data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered to match the <term> style </term> and/or <term> topic </term> of the target <term> recognition task </term> , but also that it is possible to get bigger performance gains from the <term> data </term> by using <term> class-dependent interpolation </term> of <term> N-grams </term> .
lr,43-2-N03-2003,bq In this paper , we show how <term> training data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered to match the <term> style </term> and/or <term> topic </term> of the target <term> recognition task </term> , but also that it is possible to get bigger performance gains from the <term> data </term> by using <term> class-dependent interpolation </term> of <term> N-grams </term> .
other,27-1-N03-4004,bq The <term> TAP-XL Automated Analyst 's Assistant </term> is an application designed to help an <term> English-speaking </term> analyst write a <term> topical report </term> , culling information from a large inflow of <term> multilingual , multimedia data </term> .
It gives users the ability to spend their time finding more data relevant to their task , and gives them translingual reach into other <term> languages </term> by leveraging <term> human language technology </term> .
other,15-3-N03-4010,bq The operation of the <term> system </term> will be explained in depth through browsing the <term> repository </term> of <term> data objects </term> created by the <term> system </term> during each <term> question answering session </term> .
other,13-1-P03-1005,bq This paper proposes the <term> Hierarchical Directed Acyclic Graph ( HDAG ) Kernel </term> for <term> structured natural language data </term> .
other,15-1-P03-1009,bq Previous research has demonstrated the utility of <term> clustering </term> in inducing <term> semantic verb classes </term> from undisambiguated <term> corpus data </term> .
other,30-4-P03-1009,bq A novel <term> evaluation scheme </term> is proposed which accounts for the effect of <term> polysemy </term> on the <term> clusters </term> , offering us a good insight into the potential and limitations of <term> semantically classifying </term><term> undisambiguated SCF data </term> .
lr,13-4-P03-1033,bq Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> .
lr,14-1-P03-1058,bq A central problem of <term> word sense disambiguation ( WSD ) </term> is the lack of <term> manually sense-tagged data </term> required for <term> supervised learning </term> .
lr,11-2-P03-1058,bq In this paper , we evaluate an approach to automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel corpora </term> , which are then used for disambiguating the <term> nouns </term> in the <term> SENSEVAL-2 English lexical sample task </term> .
tech,5-3-P03-1058,bq Our investigation reveals that this <term> method of acquiring sense-tagged data </term> is promising .
lr,37-4-P03-1058,bq On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .
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