| it is also time consuming to document . | Given | the development of storage media and networks | #17 Oral communication is ubiquitous and carries important information yet it is also time consuming to document. Given the development of storage media and networks one could just record and store a conversation for documentation. | ||
| translation output </term> . Subjects were | given | a set of up to six extracts of translated | #684 Subjects were given a set of up to six extracts of translated newswire text. | ||
| translation outputs </term> . The subjects were | given | three minutes per extract to determine | #715 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation. | ||
| possible <term> sentence plans </term> for a | given | <term> text-plan input </term> . Second , the | #1396 First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a given text-plan input. | ||
| <term> wide coverage English grammar </term> are | given | . While <term> paraphrasing </term> is critical | #1752 The results of a practical evaluation of this method on a wide coverage English grammar are given. | ||
| <term> off-the-shelf classifiers </term> to | give | <term> utterance classification performance | #2235 The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. | ||
| </term> as either coherent or incoherent ( | given | a <term> baseline </term> of 54.55 % ) . We | #2532 An evaluation of our system against the annotated data shows 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%). | ||
| together , the resulting <term> tagger </term> | gives | a 97.24 % <term> accuracy </term> on the <term> | #2988 Using these ideas together, the resulting taggergives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. | ||
| inflow of multilingual , multimedia data . It | gives | users the ability to spend their time finding | #3606 It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology. | ||
| finding more data relevant to their task , and | gives | them translingual reach into other <term> | #3623 It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology. | ||
| likely <term> answer candidates </term> from the | given | <term> text corpus </term> . The operation | #3681 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus. | ||
| genre </term> . Examples and results will be | given | for <term> Arabic </term> , but the approach | #4517 Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. | ||
| probable <term> morpheme sequence </term> for a | given | <term> input </term> . The <term> language model | #4688 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. | ||
| performance of a <term> summarizer </term> , at times | giving | it a significant lead over <term> non-Bayesian | #5421 It is found that the Bayesian approach generally leverages performance of a summarizer, at times giving it a significant lead over non-Bayesian models. | ||
| reranking </term> . The <term> model </term> | gives | an <term> F-measure improvement </term> of | #5529 The modelgives an F-measure improvement of [?] 1.25% beyond the base parser, and an [?] 0.25% improvement beyond the Collins (2000) reranker. | ||
| evaluations have shown , that <term> SMT </term> | gives | competitive results to <term> rule-based | #6777 Over the last few years dramatic improvements have been made, and a number of comparative evaluations have shown, that SMTgives competitive results to rule-based translation systems, requiring significantly less development time. | ||
| domains </term> . This workshop is intended to | give | an introduction to <term> statistical machine | #6812 This workshop is intended to give an introduction to statistical machine translation with a focus on practical considerations. | ||
| extent <term> entailment </term> . Our technique | gives | a substantial improvement in <term> paraphrase | #7472 Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments. | ||
| described . Moreover , some examples are | given | that underline the necessity of integrating | #7830 Moreover, some examples are given that underline the necessity of integrating some kind of information other than grammar sensu stricto into the treebank. | ||
| <term> maximum entropy classifier </term> that , | given | a <term> pair of sentences </term> , can reliably | #8371 We train a maximum entropy classifier that, given a pair of sentences, can reliably determinewhether or not they are translations of each other. |
