| To support engaging human users in robust , <term> mixed-initiative speech dialogue interactions </term> which reach beyond current capabilities in <term> dialogue systems </term> , the <term> DARPA Communicator program </term> [ 1 ] is funding the development of a <term> distributed message-passing infrastructure </term> for <term> dialogue systems </term> which all <term> Communicator </term> participants are using . | #251 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using. | |
| We tested this to see if similar criteria could be elicited from duplicating the experiment using <term> machine translation output </term> . | #677 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output. | |
| The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </term> . | #1110 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. | |
| Actually , the <term> oracle </term> acts like a <term> dynamic combiner </term> with <term> hard decisions </term> using the <term> reference </term> . | #1127 Actually, the oracle acts like a dynamic combiner with hard decisionsusing the reference. | |
| We suggest a method that mimics the behavior of the <term> oracle </term> using a <term> neural network </term> or a <term> decision tree </term> . | #1163 We suggest a method that mimics the behavior of the oracleusing a neural network or a decision tree. | |
| The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> . | #2247 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. | |
| In this paper , we show how <term> training data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered to match the <term> style </term> and/or <term> topic </term> of the target <term> recognition task </term> , but also that it is possible to get bigger performance gains from the <term> data </term> by using <term> class-dependent interpolation </term> of <term> N-grams </term> . | #3073 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. | |
| The two <term> evaluation measures </term> of the <term> BLEU score </term> and the <term> NIST score </term> demonstrated the effect of using an out-of-domain <term> bilingual corpus </term> and the possibility of using the <term> language model </term> . | #3139 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model. | |
| The two <term> evaluation measures </term> of the <term> BLEU score </term> and the <term> NIST score </term> demonstrated the effect of using an out-of-domain <term> bilingual corpus </term> and the possibility of using the <term> language model </term> . | #3148 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model. | |
| A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging </term> using <term> concept-based seeds </term> and <term> successive learners </term> is presented . | #3296 A novel bootstrapping approach to Named Entity (NE) taggingusing concept-based seeds and successive learners is presented. | |
| During <term> training </term> , the <term> blocks </term> are learned from <term> source interval projections </term> using an underlying <term> word alignment </term> . | #3455 During training, the blocks are learned from source interval projectionsusing an underlying word alignment. | |
| We describe a new approach which involves clustering <term> subcategorization frame ( SCF ) </term> distributions using the <term> Information Bottleneck </term> and <term> nearest neighbour </term> methods . | #3916 We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. | |
| Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> . | #4362 Moreover, the models are automatically derived by decision tree learningusing real dialogue data collected by the system. | |
| Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised component </term> . | #4551 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. | |
| <term> Task-based evaluation </term> using <term> Arabic information retrieval </term> indicates an improvement of 22-38 % in <term> average precision </term> over <term> unstemmed text </term> , and 96 % of the performance of the proprietary <term> stemmer </term> above . | #4571 Task-based evaluationusing Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above. | |
| The evaluation using another 23 subjects showed that the proposed method could effectively generate proper <term> referring expressions </term> . | #5713 The evaluation using another 23 subjects showed that the proposed method could effectively generate proper referring expressions. | |
| This paper proposes a new methodology to improve the <term> accuracy </term> of a <term> term aggregation system </term> using each author 's text as a coherent <term> corpus </term> . | #6129 This paper proposes a new methodology to improve the accuracy of a term aggregation systemusing each author's text as a coherent corpus. | |
| Furthermore , we present a standalone system that resolves <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence of <term> preprocessing modules </term> that mimics the manual <term> annotation process </term> . | #7083 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. | |
| We describe a <term> method </term> for identifying systematic <term> patterns </term> in <term> translation data </term> using <term> part-of-speech tag sequences </term> . | #7639 We describe a method for identifying systematic patterns in translation datausing part-of-speech tag sequences. | |
| We present the first known <term> empirical test </term> of an increasingly common speculative claim , by evaluating a representative <term> Chinese-to-English SMT model </term> directly on <term> word sense disambiguation performance </term> , using standard <term> WSD evaluation methodology </term> and <term> datasets </term> from the <term> Senseval-3 Chinese lexical sample task </term> . | #7814 We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. |
