| other,27-3-H01-1041,bq | 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,bq | 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,bq | 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,8-10-H01-1042,bq | 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,bq | <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,bq | 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,bq | <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,bq | 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,bq | <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,bq | </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,bq | approach yields <term> phrasal and single | word | lexical paraphrases </term> as well as <term> | #1806 Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases. |
| measure(ment),20-3-N03-1018,bq | significantly reduce <term> character and | word | error rate </term> , and provide evaluation | #2766 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. |
| other,66-1-N03-1033,bq | ) fine-grained modeling of <term> unknown | word | features </term> . Using these ideas together | #2976 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,bq | <term> syntax-based constraint </term> for <term> | word | alignment </term> , known as the <term> cohesion | #3234 We present a syntax-based constraint forword alignment, known as the cohesion constraint. |
| model,14-4-N03-2036,bq | projections </term> using an underlying <term> | word | alignment </term> . We show experimental | #3458 During training, the blocks are learned from source interval projections using an underlyingword alignment. |
| other,11-1-P03-1051,bq | </term> by a <term> model </term> that a <term> | word | </term> consists of a sequence of <term> morphemes | #4611 We approximate Arabic's rich morphology by a model that aword consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
| tech,22-2-P03-1051,bq | algorithm </term> to build the <term> Arabic | word | segmenter </term> from a large <term> unsegmented | #4661 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. |
| other,20-5-P03-1051,bq | <term> stems </term> from a 155 million <term> | word | </term><term> unsegmented corpus </term> , | #4726 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 millionword unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
| tech,2-6-P03-1051,bq | corpus </term> . The resulting <term> Arabic | word | segmentation system </term> achieves around | #4746 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens. |
| other,19-6-P03-1051,bq | test corpus </term> containing 28,449 <term> | word | tokens </term> . We believe this is a state-of-the-art | #4762 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens. |
