Open AccessDeep Neural Architecture for Recovering Dropped Pronouns in Korean
Author(s) -
Jung Sangkeun,
Lee Changki
Publication year - 2018
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2017-0085
Subject(s) - computer science , natural language processing , preprocessor , recurrent neural network , artificial intelligence , pronoun , machine translation , sentence , task (project management) , phone , speech recognition , artificial neural network , linguistics , philosophy , management , economics
Pronouns are frequently dropped in Korean sentences, especially in text messages in the mobile phone environment. Restoring dropped pronouns can be a beneficial preprocessing task for machine translation, information extraction, spoken dialog systems, and many other applications. In this work, we address the problem of dropped pronoun recovery by resolving two simultaneous subtasks: detecting zero‐pronoun sentences and determining the type of dropped pronouns. The problems are statistically modeled by encoding the sentence and classifying types of dropped pronouns using a recurrent neural network (RNN) architecture. Various RNN‐based encoding architectures were investigated, and the stacked RNN was shown to be the best model for Korean zero‐pronoun recovery. The proposed method does not require any manual features to be implemented; nevertheless, it shows good performance.