| 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,16-3-P06-4011,bq | <term> Web </term> and building a <term> language | model | </term> of <term> abstract moves </term> . We | #11754 The method involves automatically gathering a large number of abstracts from the Web and building a language model of abstract moves. |
| 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,3-4-C92-4207,bq | <term> world </term> . To reconstruct the <term> | model | </term> , the authors extract the <term> qualitative | #18457 To reconstruct themodel, the authors extract the qualitative spatial constraints from the text, and represent them as the numerical constraints on the spatial attributes of the entities. |
| 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,7-7-J05-1003,bq | log-likelihood </term> under a <term> baseline | model | </term> ( that of <term> Collins [ 1999 ] </term> | #8807 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. |
| 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,34-7-J05-1003,bq | were not included in the original <term> | model | </term> . The new <term> model </term> achieved | #8833 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the originalmodel. |
| 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. |
| other,20-1-P06-2110,bq | word vectors </term> in the <term> vector space | model | </term> . Through two experiments , three | #11492 This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model. |
| 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. | |
| 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,2-3-C94-1061,bq | The underlying <term> concurrent computation | model | </term> relies upon the <term> actor paradigm | #20858 The underlying concurrent computation model relies upon the actor paradigm. |
| model,26-3-N03-1001,bq | of <term> recognition </term> with this <term> | model | </term> is then passed to a <term> phone-string | #2281 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with thismodel is then passed to a phone-string classifier. |
| tech,1-6-A94-1007,bq | of the <term> parallelism </term> . The <term> | model | </term> is based on a <term> balance matching | #19816 Themodel is based on a balance matching operation for two lists of the feature sets, which provides four effects: the reduction of analysis cost, the improvement of word disambiguation, the interpretation of ellipses, and robust analysis. |
| other,1-7-P86-1038,bq | denotational semantics </term> . This <term> logical | model | </term> yields a calculus of <term> equivalences | #14774 This logical model yields a calculus of equivalences, which can be used to simplify formulas. |
| other,1-2-C88-2130,bq | linguistically by Linde ( 1974 ) . The <term> | model | </term> is embodied in a program , <term> APT | #15467 Themodel is embodied in a program, APT, that can reproduce segments of actual tape-recorded descriptions, using organizational and discourse strategies derived through analysis of our corpus. |
| model,4-3-P03-1051,bq | algorithm </term> uses a <term> trigram language | model | </term> to determine the most probable <term> | #4676 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. |
| 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,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. |
