| other,18-1-P01-1009,bq | markers </term> , which includes <term> other ( | than | ) </term> , <term> such ( as ) </term> , and <term> | #1835 This paper presents a formal analysis for a large class of words called alternative markers, which includes other ( than), such (as), and besides. |
| document descriptors ( keywords ) </term> | than | single <term> words </term> are . This leads | #20040 One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords)than single words are. | |
| trainable sentence planner </term> performs better | than | the <term> rule-based systems </term> and the | #2108 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. | |
| has been considered to be more complicated | than | <term> analysis </term> and <term> generation | #18062 The transfer phase in machine translation (MT) systems has been considered to be more complicated than analysis and generation, since it is inherently a conglomeration of individual lexical rules. | |
| theoretical accounts actually have worse coverage | than | accounts based on processing . Finally | #21186 This paper reviews the theoretical literature, and shows why many of the theoretical accounts actually have worse coverage than accounts based on processing. | |
| the proposed approach is more describable | than | other approaches such as those employing | #16412 We show that the proposed approach is more describable than other approaches such as those employing a traditional generative phonological approach. | |
| a more effective <term> CFG filter </term> | than | that of <term> LTAG </term> . We also investigate | #5135 We demonstrate that an approximation of HPSG produces a more effective CFG filterthan that of LTAG. | |
| </term> with <term> in-degree </term> greater | than | one and <term> out-degree </term> greater than | #3184 For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. | |
| <term> kanji-kana characters </term> is greater | than | that of <term> erroneous chains </term> . From | #20709 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| than one and <term> out-degree </term> greater | than | one . We create a <term> word-trie </term> | #3189 For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. | |
| 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. | |
| Surprisingly , learning <term> phrases </term> longer | than | three <term> words </term> and learning <term> | #2635 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. | |
| SMT models </term> to be significantly lower | than | that of all the dedicated <term> WSD models | #7934 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. | |
| simultaneously using less <term> memory </term> | than | is required by current <term> decoder </term> | #9149 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. | |
| . 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. | |
| simpler set of <term> model parameters </term> | than | similar <term> phrase-based models </term> | #3412 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. | |
| that the system yields higher performance | than | a <term> baseline </term> on all three aspects | #6746 Results indicate that the system yields higher performance than a baseline on all three aspects. | |
| significantly better <term> translation quality </term> | than | the <term> statistical machine translation | #9380 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> edges </term> adjacent to it , rather | than | all such <term> edges </term> as in conventional | #17624 As each new edge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments. | |
| becomes a crucial issue recently . Rather | than | using <term> length-based or translation-based | #20543 Rather than using length-based or translation-based criterion, a part-of-speech-based criterion is proposed. |
