lingvo PosTagger NER ru dnn

Posazz

Parte del etiquetado del habla de las palabras en idioma ruso utilizando una red neuronal profunda en C# para .NET

Una red neuronal profunda basada en tensores utilizada para el texto de etiqueta POS (tarea de marcado de secuencia) en ruso basado en terminaciones de palabras. Admite la computación de CPU y GPU.

Métricas para los modelos incluye:

Corpus de marcado personalizado (Sents = 41 989):

 Common-F-Score = '89.41'

Adjective          : F-score = '90.11' Precision = '88.65' Recall = '91.62'
AdjectivePronoun   : F-score = '87.77' Precision = '88.18' Recall = '87.37'
Adverb             : F-score = '85.78' Precision = '86.04' Recall = '85.51'
AdverbialParticiple: F-score = '91.01' Precision = '92.47' Recall = '89.58'
AdverbialPronoun   : F-score = '83.15' Precision = '85.71' Recall = '80.74'
AuxiliaryVerb      : F-score = '93.38' Precision = '95.48' Recall = '91.36'
Conjunction        : F-score = '90.20' Precision = '88.89' Recall = '91.55'
Infinitive         : F-score = '97.38' Precision = '96.97' Recall = '97.80'
Interjection       : F-score = '80.00' Precision = '93.33' Recall = '70.00'
Noun               : F-score = '97.13' Precision = '97.45' Recall = '96.81'
Numeral            : F-score = '93.60' Precision = '93.78' Recall = '93.41'
Other              : F-score = '77.41' Precision = '80.76' Recall = '74.32'
Participle         : F-score = '68.52' Precision = '71.58' Recall = '65.71'
Particle           : F-score = '80.78' Precision = '83.27' Recall = '78.44'
PossessivePronoun  : F-score = '92.47' Precision = '90.39' Recall = '94.65'
Predicate          : F-score = '92.57' Precision = '91.33' Recall = '93.84'
Preposition        : F-score = '98.58' Precision = '98.07' Recall = '99.09'
Pronoun            : F-score = '91.82' Precision = '91.58' Recall = '92.05'
Punctuation        : F-score = '99.87' Precision = '99.83' Recall = '99.91'
Verb               : F-score = '96.76' Precision = '96.42' Recall = '97.10'

The number of part of speech categories = '20'

"nerus_lenta.conllu" corpus (sents = 8 066 461):

 Common-F-Score = '95.11'

ADJ  : F-score = '97.79' Precision = '97.09' Recall = '98.51'
ADP  : F-score = '99.90' Precision = '99.84' Recall = '99.96'
ADV  : F-score = '98.03' Precision = '98.75' Recall = '97.33'
AUX  : F-score = '99.35' Precision = '99.30' Recall = '99.40'
CCONJ: F-score = '99.64' Precision = '99.47' Recall = '99.82'
DET  : F-score = '97.24' Precision = '96.83' Recall = '97.64'
INTJ : F-score = '58.33' Precision = '77.78' Recall = '46.67'
NOUN : F-score = '98.19' Precision = '96.99' Recall = '99.42'
NUM  : F-score = '98.66' Precision = '99.04' Recall = '98.28'
PART : F-score = '98.21' Precision = '98.69' Recall = '97.74'
PRON : F-score = '98.75' Precision = '99.22' Recall = '98.29'
PROPN: F-score = '93.65' Precision = '98.27' Recall = '89.45'
PUNCT: F-score = '99.95' Precision = '99.95' Recall = '99.95'
SCONJ: F-score = '99.29' Precision = '99.22' Recall = '99.36'
SYM  : F-score = '86.54' Precision = '89.11' Recall = '84.11'
VERB : F-score = '98.47' Precision = '98.76' Recall = '98.19'
X    : F-score = '94.86' Precision = '94.52' Recall = '95.20'

The number of categories = '17'

INCLUIDA MUESTRA DE UI POSTOSGRA:

Ner

Reconocimiento de la entidad nombrada en idioma ruso utilizando una red neuronal profunda en C# para .NET

Métricas para los modelos incluye:

"nerus_lenta.conllu" corpus (sents = 500 000):

 Common-F-Score = '94.30'

B-LOC: F-score = '97.37' Precision = '97.88' Recall = '96.87'
B-ORG: F-score = '92.90' Precision = '93.34' Recall = '92.47'
B-PER: F-score = '96.21' Precision = '97.37' Recall = '95.08'
I-LOC: F-score = '91.90' Precision = '94.68' Recall = '89.28'
I-ORG: F-score = '90.43' Precision = '89.45' Recall = '91.43'
I-PER: F-score = '96.98' Precision = '97.54' Recall = '96.42'

The number of categories = '6'

"nerus_lenta.conllu" corpus (sents = 1 000 000):

 Common-F-Score = '96.78'

B-LOC: F-score = '98.46' Precision = '98.54' Recall = '98.39'
B-ORG: F-score = '95.22' Precision = '96.10' Recall = '94.35'
B-PER: F-score = '98.71' Precision = '99.02' Recall = '98.40'
I-LOC: F-score = '94.67' Precision = '95.63' Recall = '93.73'
I-ORG: F-score = '94.43' Precision = '94.92' Recall = '93.95'
I-PER: F-score = '98.94' Precision = '98.84' Recall = '99.04'

The number of categories = '6'

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