| ngram TM ) </term> , is further proposed to | model | the <term> transliteration process </term> | #5797 Under this framework, a joint source-channel transliteration model, also called n-gram transliteration model (ngram TM), is further proposed to model the transliteration process. | |
| </term> . There are several approaches that | model | <term> information extraction </term> as a <term> | #10804 There are several approaches that model information extraction as a token classification task, using various tagging strategies to combine multiple tokens. | |
| KPSG provides </term> an explicit development | model | for constructing a computational <term> phonological | #16389 The approach of KPSG provides an explicit development model for constructing a computational phonological system: speech recognition and synthesis system. | |
| authors try to reconstruct the geometric | model | of the global scene from the scenic descriptions | #18417 In order to understand the described world, the authors try to reconstruct the geometric model of the global scene from the scenic descriptions drawing a space. | |
| understanding </term> . The authors propose a | model | for analyzing <term> English sentences </term> | #19679 The authors propose a model for analyzing English sentences including coordinate conjunctions such as and, or, but and the equivalent words. | |
| abstracting industry </term> . This paper proposes a | model | using <term> associative processors ( APs | #20197 This paper proposes a model using associative processors (APs) for real-time spoken language translation. | |
| response </term> . We have already proposed a | model | , <term> TDMT ( Transfer-Driven Machine Translation | #20233 We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation and target word selection. | |
| by <term> extrapolation </term> . Thus , our | model | , <term> TDMT on APs </term> , meets the vital | #20351 Thus, our model, TDMT on APs, meets the vital requirements of spoken language translation. | |
| grammar model </term> is considered . The | model | is based on full <term> lexicalization </term> | #21012 The model is based on full lexicalization, head-orientation via valency constraints and dependency relations, inheritance as a means for non-redundant lexicon specification, and concurrency of computation. | |
| measure(ment),1-8-H90-1060,bq | target speaker </term> . Each <term> reference | model | </term> is transformed to the <term> space </term> | #17172 Each reference model is transformed to the space of the target speaker and combined by averaging. |
| measure(ment),18-8-J05-1003,bq | F-measure </term> error over the <term> baseline | model | ’s score </term> of 88.2 % . The article | #8854 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. |
| model,1-1-C94-1061,bq | </term> and <term> Spanish </term> . A <term> grammar | model | </term> for <term> concurrent , object-oriented | #20815 A grammar model for concurrent, object-oriented natural language parsing is introduced. |
| model,1-2-H94-1014,bq | </term> or <term> paragraph </term> . The <term> | model | </term> is an <term> m-component mixture </term> | #21233 Themodel is an m-component mixture of trigram models. |
| model,1-2-N03-1018,bq | </term> of an <term> OCR system </term> . The <term> | model | </term> is designed for use in <term> error | #2713 Themodel is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks. |
| model,1-2-P03-1050,bq | Arabic ) stemmer </term> . The <term> stemming | model | </term> is based on <term> statistical machine | #4448 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. |
| model,1-2-P05-1069,bq | machine translation ( SMT ) </term> . The <term> | model | </term> predicts <term> blocks </term> with orientation | #9574 Themodel predicts blocks with orientation to handle local phrase re-ordering. |
| model,1-3-C94-1080,bq | computation </term> . The <term> computation | model | </term> relies upon the <term> actor paradigm | #21043 The computation model relies upon the actor paradigm, with concurrency entering through asynchronous message passing between actors. |
| model,1-3-H05-1012,bq | performance </term> . The <term> probabilistic | model | </term> used in the <term> alignment </term> | #7296 The probabilistic model used in the alignment directly models the link decisions. |
| model,1-4-P03-1051,bq | given <term> input </term> . The <term> language | model | </term> is initially estimated from a small | #4691 The language model is initially estimated from a small manually segmented corpus of about 110,000 words. |
| model,10-2-H92-1016,bq | </term> , the use of a <term> bigram language | model | </term> in conjunction with a <term> probabilistic | #18721 These include context-dependent phonetic modelling, the use of a bigram language model in conjunction with a probabilistic LR parser, and refinements made to the lexicon. |
