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Table 2: Confusionmatrix for English (En) and French(Fr). Class

in Université de Montréal
by C Olga Feiguina, Université De Montréal
"... In PAGE 92: ...Table2... ..."

Table 1. Preferred and synonym SNOMED terms. Code Class French term English term

in Automatic Acquisition of Morphological Knowledge for Medical Language Processing
by Pierre Zweigenbaum, Natalia Grabar 1999
"... In PAGE 2: ... These synonyms are agged with a `02 apos;, `03 apos; or `05 apos; class code, while the preferred term is agged with a `01 apos; class code. Table1 lists Table 1. Preferred and synonym SNOMED terms.... ..."
Cited by 5

Table 1. Input language data and output morphological knowledge. a. Preferred and synonym SNOMED terms. b. Morphological knowledge acquired. Code Class French term English term

in Automatic Acquisition of Morphological Knowledge for Medical Language Processing
by Pierre Zweigenbaum, Natalia Grabar 1999
Cited by 5

Table 5 Descriptive statistics of French IV respondents

in unknown title
by unknown authors
"... In PAGE 14: ... This included information on their current academic class (3 for Juniors, 4 for Seniors), years of French study, e-book effectiveness, e-book reader feature importance, the number of minutes per chapter each student spent reading both novels, and the scores on the multiple-choice and free-response assessments for each novel. Table5 gives the descriptive statistics for the French IV respondents on academic class, years of French study, e-book effectiveness, and e-book reader feature importance. 5.... ..."

Table 2. Conflation and distribution patterns within the mapping of meta-schemas and form-classes in the three language types (exemplified by Swedish, French and Thai).

in Motion Event Typology Meets Computational Modelling
by Bengt Sigurd, Jordan Zlatev

Table I. French variants of an English number, and translation probabilities for the first invariant pair of each class found in a given pair of translated segments.

in Target-Text Mediated Interactive Machine Translation
by George Foster, Pierre Isabelle, Pierre Plamondon 1997
Cited by 11

Table 4 Congruence Classes. The languages are, from left to right, English, Dutch, German, French, Spanish, Catalan, Russian, Chinese, Korean, and Japanese.

in f Machine Learning Comprehension Grammars for Ten Languages
by Lin Liang
"... In PAGE 17: ...nondcnoting words in Table4 is large. We cannot discuss in detail all of these particles.... ..."

Table 4 Descriptive statistics of AP French Literature respondents

in unknown title
by unknown authors
"... In PAGE 14: ... Results All sixteen students from both classes responded to the surveys. Table4 gives the descriptive statistics for the AP French Literature respondents. This included information on their current academic class (3 for Juniors, 4 for Seniors), years of French study, e-book effectiveness, e-book reader feature importance, the number of minutes per chapter each student spent reading both novels, and the scores on the multiple-choice and free-response assessments for each novel.... ..."

Table 2: Left: Single word homophones in French (BREF), En- glish (WSJ), German (FR) and Italian (S24o). Right: Table entries correspond to the number of homophone classes with k graphemic forms in the class.

in Speech Recognition Of European Languages
by Lori Lamel, Renato De Mori 1995
"... In PAGE 3: ....3. Homophones The problem of having words with different orthography but the same phonetic representationincreasesthe complexity of the recog- nition task. Table2 gives lexical homophone data for some popu- lar speech corpora used in Europe. It appears as expected that the problem is less important in Italian than, for example, in French.... ..."
Cited by 5

Table 3: Total number of documents in our English, French, Russian, and German collections. The numbers of classes, subclasses, and main groups covered is also indicated in each case. Categories with insufficient numbers of training documents are not retained for categorization tests.

in cui.unige.ch
by C. J. Fall, K. Benzineb, Metaread Sa, Rue Eugène-marziano, Ch- Genève-acacias, J. Guyot, A. Törcsvári, P. Fiévet
"... In PAGE 8: ....J.Fall et al to be able to predict IPC symbols in areas that describe technology that is not used frequently any more or where novelties are increasingly rare. Statistics of the document distributions are indicated in Table3 . We note that despite having access to several hundred thousand documents, we do not have a sufficiently uniform distribution of documents to be able to make reliable predictions across all the IPC categories, particularly for Russian and German at main ... ..."
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