| non-native language essays </term> in less | than | 100 <term> words </term> . Even more illuminating | #644 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. | ||
| . Our <term> algorithm </term> reported more | than | 99 % <term> accuracy </term> in both <term> language | #1281 Our algorithm reported more than 99% accuracy in both language identification and key prediction. | ||
| rating </term> on average is only 5 % worse | than | the <term> top human-ranked sentence plan | #1454 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. | ||
| alternative markers </term> , which includes other ( | than | ) , such ( as ) , and besides . These <term> | #1836 This paper presents a formal analysis for a large class of words called alternative markers, which includes other ( than), such (as), and besides. | ||
| trainable sentence planner </term> performs better | than | the <term> rule-based systems </term> and the | #2109 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. | ||
| Surprisingly , learning <term> phrases </term> longer | than | three <term> words </term> and learning <term> | #2636 Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. | ||
| </term> with <term> in-degree </term> greater | than | one and <term> out-degree </term> greater than | #3185 For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. | ||
| than one and <term> out-degree </term> greater | than | one . We create a <term> word-trie </term> | #3190 For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. | ||
| simpler set of <term> model parameters </term> | than | similar <term> phrase-based models </term> | #3413 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parametersthan similar phrase-based models. | ||
| a more effective <term> CFG filter </term> | than | that of <term> LTAG </term> . We also investigate | #5137 We demonstrate that an approximation of HPSG produces a more effective CFG filterthan that of LTAG. | ||
| generalize naturally to NLP structures other | than | <term> parse trees </term> . This paper presents | #5577 Although our experiments are focused on parsing, the techniques described generalize naturally to NLP structures other than parse trees. | ||
| score </term> that is significantly higher | than | that of the <term> baseline </term> . Following | #5900 In our experiments, the method achieves a TRDR score that is significantly higher than that of the baseline. | ||
| SMT models </term> to be significantly lower | than | that of all the dedicated <term> WSD models | #6477 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. | ||
| integrating some kind of information other | than | <term> grammar </term> sensu stricto into the | #7842 Moreover, some examples are given that underline the necessity of integrating some kind of information other than grammar sensu stricto into the treebank. | ||
| significantly higher <term> accuracy </term> | than | a state-of-the-art <term> coherence model | #8663 Our experiments demonstrate that the induced model achieves significantly higher accuracythan a state-of-the-art coherence model. | ||
| simultaneously using less <term> memory </term> | than | is required by current <term> decoder implementations | #8787 In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memorythan is required by current decoder implementations. | ||
| labelled bracket F-score </term> of 76.2 , higher | than | previously reported results on the <term> | #8976 In this paper, we present an unlexicalized parser for German which employs smoothing and suffix analysis to achieve a labelled bracket F-score of 76.2, higher than previously reported results on the NEGRA corpus. | ||
| significantly better <term> translation quality </term> | than | the <term> statistical machine translation | #9237 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation qualitythan the statistical machine translation system alone. | ||
| </term> yields a lower <term> error rate </term> | than | the <term> HMM and Maxent models </term> on | #9544 In general, our CRF model yields a lower error ratethan the HMM and Maxent models on the NIST sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. | ||
| </term> which is an order of magnitude smaller | than | <term> Penn WSJ </term> . We present a <term> | #10562 We explored possible ways to obtain a compact lexicon, consistent with CCG principles, from a treebank which is an order of magnitude smaller than Penn WSJ. |
