| translation systems </term> . This , the first | experiment | in a series of experiments , looks at the | #613 This, the first experiment in a series of experiments, looks at the intelligibility of MT output. | ||
| This , the first experiment in a series of | experiments | , looks at the <term> intelligibility </term> | #618 This, the first experiment in a series of experiments, looks at the intelligibility of MT output. | ||
| other,1-4-H01-1042,ak | output </term> . A <term> language learning | experiment | </term> showed that <term> assessors </term> | #631 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. | |
| criteria could be elicited from duplicating the | experiment | using <term> machine translation output </term> | #676 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output. | ||
| made this decision . The results of this | experiment | , along with a preliminary analysis of | #759 The results of this experiment, along with a preliminary analysis of the factors involved in the decision making process will be presented here. | ||
| tech,5-1-P01-1070,ak | set of <term> supervised machine learning | experiments | </term> centering on the construction of <term> | #2133 We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. | |
| , passage , and/or answer levels </term> . | Experiments | evaluating the effectiveness of our <term> | #2397 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. Experiments evaluating the effectiveness of our answer resolution algorithm show 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. | ||
| other,3-3-N03-1012,ak | </term> . We conducted an <term> annotation | experiment | </term> and showed that <term> human annotators | #2484 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. | |
| framework , we carry out a large number of | experiments | to understand better and explain why <term> | #2576 Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. | ||
| monolingual corpus </term> . We conducted | experiments | with an <term> EBMT system </term> . The two | #3118 We conducted experiments with an EBMT system. | ||
| kernel function </term> . The results of the | experiments | demonstrate that the <term> HDAG Kernel </term> | #3869 The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods. | ||
| <term> discourse understanding process </term> . | Experiment | results have shown that a <term> system </term> | #4256 Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process. Experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective. | ||
| Collins ( 2000 ) reranker </term> . Although our | experiments | are focused on <term> parsing </term> , the | #5562 Although our experiments are focused on parsing, the techniques described generalize naturally to NLP structures other than parse trees. | ||
| IDF-weighted word overlap </term> . In our | experiments | , the method achieves a <term> TRDR score | #5888 In our experiments, the method achieves a TRDR score that is significantly higher than that of the baseline. | ||
| field , which we address with this work . | Experiments | with the <term> TREC 2003 and TREC 2004 QA | #5996 The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. Experiments with the TREC 2003 and TREC 2004 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that POURPRE outperforms direct application of existing metrics. | ||
| WSD models </term> . We present controlled | experiments | showing the <term> WSD accuracy </term> of | #6463 We present controlled experiments showing the WSD accuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered. | ||
| over all of the other models used in the | experiments | . Towards <term> deep analysis </term> of <term> | #7489 Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments. | ||
| manual judgement </term> . Our preliminary | experiments | on building a <term> paraphrase corpus </term> | #7534 Our preliminary experiments on building a paraphrase corpus have so far been producing promising results, which we have evaluated according to cost-efficiency, exhaustiveness, and reliability. | ||
| the <term> paraphrases </term> produced in an | experiment | , i.e. , ( i ) their <term> grammaticality | #7600 We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correct sentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. | ||
| space </term> in the <term> parsing data </term> . | Experiments | show significant efficiency gains for the | #8252 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. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. |
