%0 Conference Paper
%A Ngo, Thi Lan
%A Pham, Khac Linh
%A Hideaki, Takeda
%A Pham, Bao Son
%A Phan, Xuan Hieu
%B The Eighth International Symposium on Information and Communication Technology (SoICT 2017)
%C Nha Trang
%D 2017
%F SisLab:2692
%T On the Identification of Suggestion Intents from Vietnamese Conversational Texts
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2692/
%X Suggestion intents mining from texts is an emerging research topic in recent years. Fully understanding suggestion intents in conversational texts is a complicated process that includes three major stages: user suggestion intents filtering, suggestion domain identification, and arguments extraction of suggestion intents. In the scope of this paper, we study the first phase, that is, building a binary classification model to determine whether a text unit carries suggestion intents or not. We come up with a new text unit to analysis suggestion, that is functional segment. According to the  ISO 24617-2 standard, a functional segment is “minimal stretch of communicative behavior that has one or more communicative functions”. We investigate two approaches to filter functional segment containing suggestion intents: machine learning using maximum entropy (Maxent) model and deep learning using convolutional neural networks (CNN) model. The results of these experiments  on Vietnamese online media texts are very promising. To the best of our knowledge, this is the first study to analyze suggestion at functional segment unit.