| tech,11-5-H01-1058,bq | show the need for a <term> dynamic language | model | combination </term> to improve the <term> performance | #1144 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. |
| model,12-3-N03-1001,bq | first used to train a <term> phone n-gram | model | </term> for a particular <term> domain </term> | #2269 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. |
| model,4-1-N03-1017,bq | propose a new <term> phrase-based translation | model | </term> and <term> decoding algorithm </term> | #2545 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. |
| model,7-1-N03-1018,bq | probabilistic optical character recognition ( OCR ) | model | </term> that describes an end-to-end process | #2682 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. |
| model,23-2-N03-1026,bq | forests </term> , and a <term> maximum-entropy | model | </term> for <term> stochastic output selection | #2832 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. |
| model,28-1-N03-2006,bq | </term> and , in addition , the <term> language | model | </term> of an in-domain <term> monolingual | #3108 In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus. |
| model,7-1-N03-2036,bq | we describe a <term> phrase-based unigram | model | </term> for <term> statistical machine translation | #3398 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models. |
| model,16-2-P03-1033,bq | kinds of <term> users </term> , the <term> user | model | </term> we propose is more comprehensive | #4315 Unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive. |
| 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,8-1-P03-1051,bq | Arabic 's rich morphology </term> by a <term> | model | </term> that a <term> word </term> consists of | #4608 We approximate Arabic's rich morphology by amodel that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
| model,5-3-C04-1103,bq | <term> joint source-channel transliteration | model | </term> , also called <term> n-gram transliteration | #5781 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. |
| tech,2-4-C04-1112,bq | algorithm . Testing the <term> lemma-based | model | </term> on the <term> Dutch SENSEVAL-2 test | #6058 Testing the lemma-based model on the Dutch SENSEVAL-2 test data, we achieve a significant increase in accuracy over the wordform model. |
| model,20-2-C04-1147,bq | <term> terms </term> , an <term> independence | model | </term> , and a <term> parametric affinity | #6342 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model. |
| tech,19-4-N04-4028,bq | field ( CRF ) </term> , a <term> probabilistic | model | </term> which has performed well on <term> | #6831 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model. |
| 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. |
| tech,1-3-H05-1095,bq | proposed . A <term> statistical translation | model | </term> is also presented that deals such | #7373 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. |
| model,18-1-I05-2021,bq | representative <term> Chinese-to-English SMT | model | </term> directly on <term> word sense disambiguation | #7806 We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. |
| tech,2-3-J05-1003,bq | these <term> parses </term> . A second <term> | model | </term> then attempts to improve upon this | #8691 A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence. |
| model,25-3-P05-1034,bq | </term> , and train a <term> tree-based ordering | model | </term> . We describe an efficient <term> decoder | #9271 We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. |
| tech,3-3-P05-1048,bq | state-of-the-art <term> Chinese word sense disambiguation | model | </term> to choose <term> translation candidates | #9354 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 quality than the statistical machine translation system alone. |
