| participants are using . In this presentation , we | describe | the features of and requirements for a | #258 In this presentation, we describe the features of and requirements for a genuinely useful software infrastructure for this purpose. | ||
| a standard <term> text browser </term> . We | describe | how this information is used in a <term> | #314 We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function. | ||
| with the best <term> confidence </term> . We | describe | a three-tiered approach for <term> evaluation | #1196 We describe a three-tiered approach for evaluation of spoken dialogue systems. | ||
| and <term> component performance </term> . We | describe | our use of this approach in numerous fielded | #1224 We describe our use of this approach in numerous fielded user studies conducted with the U.S. military. | ||
| the <term> hand-crafted system </term> . We | describe | a set of <term> supervised machine learning | #2126 We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. | ||
| among the target variables . This paper | describes | a method for <term> utterance classification | #2208 This paper describes a method for utterance classification that does not require manual transcription of training data. | ||
| character recognition ( OCR ) model </term> that | describes | an end-to-end process in the <term> noisy | #2685 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. | ||
| using the <term> language model </term> . We | describe | a simple <term> unsupervised technique </term> | #3155 We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton. | ||
| <term> NE types </term> . In this paper , we | describe | a <term> phrase-based unigram model </term> | #3395 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models. | ||
| undisambiguated <term> corpus data </term> . We | describe | a new approach which involves clustering | #3904 We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. | ||
| evaluating <term> WSD programs </term> . We | describe | the ongoing construction of a <term> large | #4935 We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information, e.g. the construction of domain-independent lexica. | ||
| over <term> non-Bayesian models </term> . We | describe | a new method for the representation of | #5431 We describe a new method for the representation of NLP structures within reranking approaches. | ||
| focused on <term> parsing </term> , the techniques | described | generalize naturally to NLP structures | #5570 Although our experiments are focused on parsing, the techniques described generalize naturally to NLP structures other than parse trees. | ||
| retrieval </term> from complex news articles | describing | multi-event stories published over time | #5761 We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. | ||
| direct application of existing metrics . We | describe | a <term> method for identifying systematic | #6030 We describe a method for identifying systematic patterns in translation data using part-of-speech tag sequences. | ||
| method developed for <term> ILIMP </term> are | described | briefly , as well as the use of <term> ILIMP | #6204 Other tasks using the method developed for ILIMP are described briefly, as well as the use of ILIMP in a modular syntactic analysis system. | ||
| million <term> words </term> . In this paper , we | describe | <term> data collection </term> , <term> transcription | #7229 In this paper, we describe data collection, transcription, word segmentation, and part-of-speech annotation of this corpus. | ||
| <term> corpus-based investigations </term> are | described | . Moreover , some examples are given that | #7823 After a thorough description of the phenomena, the results of corpus-based investigations are described. | ||
| boosting approach to ranking problems </term> | described | in Freund et al. ( 1998 ) . We apply the | #8142 We introduce a new method for the reranking task, based on the boosting approach to ranking problemsdescribed in Freund et al. (1998). | ||
| </term> is NP-hard , several approximations are | described | and empirically compared . In experiments | #8543 Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. |
