lingvo PosTagger NER ru dnn

Post-agger

Une partie du marquage de la parole des mots en langue russe à l'aide d'un réseau de neurones profonds en C # pour .NET

Un réseau neuronal profond basé sur les tenseurs utilisé pour le texte de marquage (tâche d'ébultication de séquence) en russe en fonction des terminaisons des mots. Prend en charge le CPU et l'informatique GPU.

Les mesures pour les modèles comprennent:

Corpus de balisage personnalisé (SENTES = 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'

Échantillon d'interface utilisateur post-agitation:

Nervure

Reconnaissance de l'entité nommée en langue russe à l'aide d'un réseau neuronal profond en C # pour .NET

Les mesures pour les modèles comprennent:

"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'

Corpus "NERUS_LENTA.CONLLU" (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'

Échantillon d'interface utilisateur NER: inclus: