| other,27-3-H01-1041,ak | case markers </term> , relatively <term> free | word | order </term> , and frequent omissions of | #467 The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments). | |
| tech,7-4-H01-1041,ak | quality <term> translation </term> via <term> | word | sense disambiguation </term> and accurate | #484 (ii) High quality translation viaword sense disambiguation and accurate word order generation of the target language. | |
| tech,12-4-H01-1041,ak | disambiguation </term> and accurate <term> | word | order generation </term> of the <term> target | #489 (ii) High quality translation via word sense disambiguation and accurateword order generation of the target language. | |
| other,18-4-H01-1042,ak | language essays </term> in less than 100 <term> | words | </term> . Even more illuminating was the | #646 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100words. | |
| other,8-10-H01-1042,ak | Additionally , they were asked to mark the <term> | word | </term> at which they made this decision | #747 Additionally, they were asked to mark theword at which they made this decision. | |
| other,4-3-H01-1058,ak | <term> oracle </term> knows the <term> reference | word | string </term> and selects the <term> word | #1075 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. | |
| other,10-3-H01-1058,ak | word string </term> and selects the <term> | word | string </term> with the best <term> performance | #1080 The oracle knows the reference word string and selects theword 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. | |
| measure(ment),19-3-H01-1058,ak | <term> performance </term> ( typically , <term> | word | or semantic error rate </term> ) from a list | #1089 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. | |
| other,29-3-H01-1058,ak | error rate </term> ) from a list of <term> | word | strings </term> , where each <term> word string | #1099 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 ofword strings, where each word string has been obtained by using a different LM. | |
| other,34-3-H01-1058,ak | <term> word strings </term> , where each <term> | word | string </term> has been obtained by using | #1104 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 eachword string has been obtained by using a different LM. | |
| model,24-3-P01-1004,ak | </term> superior to any of the tested <term> | word | N-gram models </term> . Further , in their | #1555 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the testedword N-gram models. | |
| other,3-3-P01-1008,ak | approach yields <term> phrasal and single | word | lexical paraphrases </term> as well as <term> | #1807 Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases. | |
| other,11-1-P01-1009,ak | formal analysis for a large class of <term> | words | </term> called <term> alternative markers </term> | #1827 This paper presents a formal analysis for a large class ofwords called alternative markers, which includes other (than), such (as), and besides. | |
| other,1-2-P01-1009,ak | such ( as ) , and besides . These <term> | words | </term> appear frequently enough in <term> | #1848 Thesewords appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. | |
| other,7-4-N03-1017,ak | <term> phrases </term> longer than three <term> | words | </term> and learning <term> phrases </term> from | #2638 Surprisingly, learning phrases longer than threewords and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. | |
| measure(ment),20-3-N03-1018,ak | significantly reduce <term> character and | word | error rate </term> , and provide evaluation | #2767 We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text. | |
| jointly conditioning on multiple consecutive | words | , ( iii ) effective use of <term> priors </term> | #2955 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | ||
| other,66-1-N03-1033,ak | ) fine-grained modeling of <term> unknown | word | features </term> . Using these ideas together | #2977 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| tech,6-1-N03-2017,ak | <term> syntax-based constraint </term> for <term> | word | alignment </term> , known as the <term> cohesion | #3235 We present a syntax-based constraint forword alignment, known as the cohesion constraint. | |
| model,14-4-N03-2036,ak | projections </term> using an underlying <term> | word | alignment </term> . We show experimental | #3459 During training, the blocks are learned from source interval projections using an underlyingword alignment. |
