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http://dx.doi.org/10.5909/JBE.2019.24.1.58

A Study on Named Entity Recognition for Effective Dialogue Information Prediction  

Go, Myunghyun (Department of Digital Contents, Sejong University)
Kim, Hakdong (Department of Digital Contents, Sejong University)
Lim, Heonyeong (Department of Digital Contents, Sejong University)
Lee, Yurim (Department of Artificial Intelligence and Linguistic Engineering, Sejong University)
Jee, Minkyu (Department of Software Convergence, Sejong University)
Kim, Wonil (Department of Software, Sejong University)
Publication Information
Journal of Broadcast Engineering / v.24, no.1, 2019 , pp. 58-66 More about this Journal
Abstract
Recognition of named entity such as proper nouns in conversation sentences is the most fundamental and important field of study for efficient conversational information prediction. The most important part of a task-oriented dialogue system is to recognize what attributes an object in a conversation has. The named entity recognition model carries out recognition of the named entity through the preprocessing, word embedding, and prediction steps for the dialogue sentence. This study aims at using user - defined dictionary in preprocessing stage and finding optimal parameters at word embedding stage for efficient dialogue information prediction. In order to test the designed object name recognition model, we selected the field of daily chemical products and constructed the named entity recognition model that can be applied in the task-oriented dialogue system in the related domain.
Keywords
Task-Oriented Dialogue System; Word Embedding; NER(Named Entity Recognition); Bi-LSTM;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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