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1. (WO2019024050) DEEP CONTEXT-BASED GRAMMATICAL ERROR CORRECTION USING ARTIFICIAL NEURAL NETWORKS
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Pub. No.: WO/2019/024050 International Application No.: PCT/CN2017/095841
Publication Date: 07.02.2019 International Filing Date: 03.08.2017
IPC:
G06F 17/27 (2006.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
17
Digital computing or data processing equipment or methods, specially adapted for specific functions
20
Handling natural language data
27
Automatic analysis, e.g. parsing, orthograph correction
Applicants:
LINGOCHAMP INFORMATION TECHNOLOGY (SHANGHAI) CO., LTD. [CN/CN]; Suite 2101, Tower 9 970 Dalian Road, Yangpu District Shanghai 200092, CN
Inventors:
LIN, Hui; CN
WANG, Chuan; CN
LI, Ruobing; CN
Agent:
HANHOW INTELLECTUAL PROPERTY PARTNERS; Suite 1919, F/19, International Technology Transfer Center No. 3 Haidian Avenue, Haidian District Beijing 100080, CN
Priority Data:
Title (EN) DEEP CONTEXT-BASED GRAMMATICAL ERROR CORRECTION USING ARTIFICIAL NEURAL NETWORKS
(FR) CORRECTION D'ERREURS DE GRAMMAIRE BASÉE SUR UN CONTEXTE PROFOND ET UTILISANT DES RÉSEAUX NEURONAUX ARTIFICIELS
Abstract:
(EN) Disclosed herein are methods and systems for grammatical error detection. In one example, a sentence is received. One or more target words in the sentence are identified based, at least in part, on one or more grammatical error types. Each of the one or more target words corresponds to at least one of the one or more grammatical error types. For at least one of the one or more target words, a classification of the target word with respect to the corresponding grammatical error type is estimated using an artificial neural network model trained for the grammatical error type. A grammatical error in the sentence is detected based, at least in part, on the target word and the estimated classification of the target word.
(FR) L'invention concerne des procédés et des systèmes de détection d'erreurs de grammaire. Dans un exemple, une phrase est reçue. Un ou plusieurs mots cibles de la phrase sont identifiés au moins en partie sur la base d'un ou plusieurs types d'erreurs de grammaire. Chacun des mots cibles correspond à au moins un des types d'erreurs de grammaire. Une classification d'au moins un des mots cibles quant au type d'erreur de grammaire correspondant est estimée à l'aide d'un modèle de réseau neuronal artificiel entraîné pour le type d'erreur de grammaire. Une erreur de grammaire dans la phrase est détectée au moins en partie sur la base du mot cible et de la classification estimée du mot cible.
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African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: English (EN)
Filing Language: English (EN)