| </term> and detect those automatically which is | shown | on a large database of TV shows . Emotions | #154 Several extensions of this basic idea are being discussed and/or evaluated: Similar to activities one can define subsets of larger database and detect those automatically which is shown on a large database of TV shows. | ||
| which is shown on a large database of TV | shows | . Emotions and other <term> indices </term> | #161 Several extensions of this basic idea are being discussed and/or evaluated: Similar to activities one can define subsets of larger database and detect those automatically which is shown on a large database of TV shows. | ||
| </term> for this purpose . In this paper we | show | how two standard outputs from <term> information | #278 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser. | ||
| <term> language learning experiment </term> | showed | that <term> assessors </term> can differentiate | #632 A language learning experimentshowed that assessors can differentiate native from non-native language essays in less than 100 words. | ||
| context of <term> dialog systems </term> . We | show | how research in <term> generation </term> can | #996 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques. | ||
| provide experimental results that clearly | show | the need for a <term> dynamic language model | #1137 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. | ||
| learned from <term> training data </term> . We | show | that the trained <term> SPR </term> learns | #1434 We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. | ||
| configuration , <term> bag-of-words methods </term> are | shown | to be equivalent to <term> segment order-sensitive | #1569 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. | ||
| on <term> queries </term> containing them . I | show | that the <term> performance </term> of a <term> | #1873 I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics. | ||
| <term> baseline sentence planners </term> . We | show | that the <term> trainable sentence planner | #2101 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system. | ||
| <term> answer resolution algorithm </term> | show | a 35.0 % <term> relative improvement </term> | #2406 Experiments evaluating the effectiveness of our answer resolution algorithmshow a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric. | ||
| an <term> annotation experiment </term> and | showed | that <term> human annotators </term> can reliably | #2486 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. | ||
| </term> against the <term> annotated data </term> | shows | that , it successfully classifies 73.2 | #2511 An evaluation of our system against the annotated datashows 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%). | ||
| </term> of <term> summarization quality </term> | shows | a close correlation between the <term> automatic | #2867 An experimental evaluation of summarization qualityshows a close correlation between the automatic parse-based evaluation and a manual evaluation of generated strings. | ||
| speech </term> are limited . In this paper , we | show | how <term> training data </term> can be supplemented | #3034 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. | ||
| different <term> algorithms </term> . The results | show | that it can provide a significant improvement | #3274 The results show that it can provide a significant improvement in alignment quality. | ||
| underlying <term> word alignment </term> . We | show | experimental results on <term> block selection | #3463 We show experimental results on block selection criteria based on unigram counts and phrase length. | ||
| twenty <term> Switchboard dialogues </term> and | show | that it compares well to Byron 's ( 2002 | #4028 We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron's (2002) manually tuned system. | ||
| process </term> . Experiment results have | shown | that a <term> system </term> that exploits | #4259 Experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective. | ||
| our laboratory . Experimental evaluation | shows | that the <term> cooperative responses </term> | #4407 Experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users. |
