| tech,7-5-H01-1041,ak | system development </term> and <term> porting to | new | domains </term> via <term> knowledge-based | #506 (iii) Rapid system development and porting to new domains via knowledge-based automated acquisition of grammars. | |
| Listen-Communicate-Show ( LCS ) </term> is a | new | paradigm for <term> human interaction with | #786 Listen-Communicate-Show (LCS) is a new paradigm for human interaction with data sources. | ||
| developing applications of this technology in | new | domains . Recent advances in <term> Automatic | #908 We have demonstrated this capability in several field exercises with the Marines and are currently developing applications of this technology in new domains. | ||
| recognition </term> has brought to light a | new | problem : as <term> dialog systems </term> | #941 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. | ||
| </term> , a <term> sentence planner </term> , and a | new | methodology for automatically training <term> | #1349 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. | ||
| baseline </term> of 54.55 % ) . We propose a | new | <term> phrase-based translation model </term> | #2543 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. | ||
| constraint-based parser/generator </term> . We present a | new | <term> part-of-speech tagger </term> that demonstrates | #2913 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | ||
| predicate-argument structures </term> . We also introduce a | new | way of automatically identifying <term> predicate | #3734 We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. | ||
| undisambiguated <term> corpus data </term> . We describe a | new | approach which involves clustering <term> | #3906 We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. | ||
| tech,17-1-P03-1030,ak | Detection and Tracking tasks </term> of <term> | new | event detection </term> . In this paper we | #4060 Link detection has been regarded as a core technology for the Topic Detection and Tracking tasks ofnew event detection. | |
| tech,9-2-P03-1030,ak | <term> story link detection </term> and <term> | new | event detection </term> as <term> information | #4073 In this paper we formulate story link detection andnew event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems. | |
| these arguments , we introduce a number of | new | performance enhancing techniques including | #4103 Motivated by these arguments, we introduce a number of new performance enhancing techniques including part of speech tagging, new similarity measures and expanded stop lists. | ||
| including <term> part of speech tagging </term> , | new | <term> similarity measures </term> and expanded | #4113 Motivated by these arguments, we introduce a number of new performance enhancing techniques including part of speech tagging, new similarity measures and expanded stop lists. | ||
| algorithm </term> for automatically acquiring | new | <term> stems </term> from a <term> 155 million | #4722 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. | ||
| non-Bayesian models </term> . We describe a | new | method for the representation of NLP structures | #5433 We describe a new method for the representation of NLP structures within reranking approaches. | ||
| building <term> translation systems </term> for | new | <term> language pairs </term> or new <term> domains | #6800 This is particularly important when building translation systems for new language pairs or new domains. | ||
| </term> for new <term> language pairs </term> or | new | <term> domains </term> . This workshop is intended | #6804 This is particularly important when building translation systems for new language pairs or new domains. | ||
| features </term> into account . We introduce a | new | method for the <term> reranking task </term> | #8127 We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). | ||
| included in the original <term> model </term> . The | new | <term> model </term> achieved 89.75 % <term> | #8201 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. | ||
| of 88.2 % . The article also introduces a | new | <term> algorithm </term> for the <term> boosting | #8231 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
