Detecting Grammatical Errors in the NTOU CGED System by Identifying Frequent Subsentences

Autor:	Chuan-Jie Lin, Shao-Heng Chen
Rok vydání:	2018
Předmět:	Computer science business.industry Selection (linguistics) Artificial intelligence computer.software_genre business computer Word (computer architecture) Natural language processing Sentence Task (project management)
Zdroj:	NLP-TEA@ACL
DOI:	10.18653/v1/w18-3730
Popis:	The main goal of Chinese grammatical error diagnosis task is to detect word er-rors in the sentences written by Chinese-learning students. Our previous system would generate error-corrected sentences as candidates and their sentence likeli-hood were measured based on a large scale Chinese n-gram dataset. This year we further tried to identify long frequent-ly-seen subsentences and label them as correct in order to avoid propose too many error candidates. Two new methods for suggesting missing and selection er-rors were also tested.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::53cee0b79d6570a6b5631f6903b4704e https://doi.org/10.18653/v1/w18-3730 Zobrazit plný text záznamu