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1. US20190332617 - Predicting Labels Using a Deep-Learning Model

Office États-Unis d'Amérique
Numéro de la demande 16505521
Date de la demande 08.07.2019
Numéro de publication 20190332617
Date de publication 31.10.2019
Type de publication A1
CIB
G06F 16/33
GPHYSIQUE
06CALCUL; COMPTAGE
FTRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES
16Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet
30de données textuelles non structurées
33Requêtes
G06N 3/04
GPHYSIQUE
06CALCUL; COMPTAGE
NSYSTÈMES DE CALCULATEURS BASÉS SUR DES MODÈLES DE CALCUL SPÉCIFIQUES
3Systèmes de calculateurs basés sur des modèles biologiques
02utilisant des modèles de réseaux neuronaux
04Architecture, p.ex. topologie d'interconnexion
CPC
G06F 16/3331
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
30of unstructured textual data
33Querying
3331Query processing
G06F 16/334
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
30of unstructured textual data
33Querying
3331Query processing
334Query execution
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
Déposants Facebook, Inc.
Inventeurs Jason E. Weston
Keith Adams
Sumit Chopra
Titre
(EN) Predicting Labels Using a Deep-Learning Model
Abrégé
(EN)

In one embodiment, a method includes receiving, from a client system, a text input comprising one or more n-grams, determining, using a deep-learning model, a vector representation of the text input based on the one or more n-grams, determining an embedding of the vector representation of the text input in a d-dimensional embedding space, identifying one or more labels based on, for each of the one or more labels, a respective similarity of an embedding of a vector representation of the label in the embedding space to the embedding of the vector representation of the text input, and sending, to the client system in response to the received text input, instructions for presenting a user interface comprising one or more of the identified labels as suggested labels.