Given the development of <term> storage media and networks </term> one could just record and store a <term> conversation </term> for documentation .
To support engaging human users in robust , <term> mixed-initiative speech dialogue interactions </term> which reach beyond current capabilities in <term> dialogue systems </term> , the <term> DARPA Communicator program </term> [ 1 ] is funding the development of a <term> distributed message-passing infrastructure </term> for <term> dialogue systems </term> which all <term> Communicator </term> participants are using .
We describe how this information is used in a <term> prototype system </term> designed to support <term> information workers </term> ' access to a <term> pharmaceutical news archive </term> as part of their <term> industry watch </term> function .
The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core modules </term> , <term> language understanding and generation modules </term> mediated by a <term> language neutral meaning representation </term> called a <term> semantic frame </term> .
The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> .
We have built and will demonstrate an application of this approach called <term> LCS-Marine </term> .
Recent advances in <term> Automatic Speech Recognition technology </term> have put the goal of naturally sounding <term> dialog systems </term> within reach .
In this paper , we address the problem of combining several <term> language models ( LMs ) </term> .
We describe a three-tiered approach for <term> evaluation </term> of <term> spoken dialogue systems </term> .
<term> Sentence planning </term> is a set of inter-related but distinct tasks , one of which is <term> sentence scoping </term> , i.e. the choice of <term> syntactic structure </term> for elementary <term> speech acts </term> and the decision of how to combine them into one or more <term> sentences </term> .
In this paper , we compare the relative effects of <term> segment order </term> , <term> segmentation </term> and <term> segment contiguity </term> on the <term> retrieval performance </term> of a <term> translation memory system </term> .
The theoretical study of the <term> range concatenation grammar [ RCG ] formalism </term> has revealed many attractive properties which may be used in <term> NLP </term> .
tech,6-1-P01-1008,bq While <term> paraphrasing </term> is critical both for <term> interpretation and generation of natural language </term> , current systems use manual or semi-automatic methods to collect <term> paraphrases </term> .
This paper presents a <term> formal analysis </term> for a large class of <term> words </term> called <term> alternative markers </term> , which includes <term> other ( than ) </term> , <term> such ( as ) </term> , and <term> besides </term> .
We provide a <term> logical definition </term> of <term> Minimalist grammars </term> , that are <term> Stabler 's formalization </term> of <term> Chomsky 's minimalist program </term> .
<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .
We describe a set of <term> supervised machine learning </term> experiments centering on the construction of <term> statistical models </term> of <term> WH-questions </term> .
This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> .
Motivated by the success of <term> ensemble methods </term> in <term> machine learning </term> and other areas of <term> natural language processing </term> , we developed a <term> multi-strategy and multi-source approach to question answering </term> which is based on combining the results from different <term> answering agents </term> searching for <term> answers </term> in multiple <term> corpora </term> .
In this paper we present <term> ONTOSCORE </term> , a system for scoring sets of <term> concepts </term> on the basis of an <term> ontology </term> .
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