eprintid: 2594
rev_number: 13
eprint_status: archive
userid: 290
dir: disk0/00/00/25/94
datestamp: 2017-10-30 04:19:59
lastmod: 2017-11-27 04:29:52
status_changed: 2017-11-27 04:29:52
type: conference_item
metadata_visibility: show
creators_name: Luong, Thai Le
creators_name: Cao, Minh Son
creators_name: Le, Duc Thang
creators_name: Phan, Xuan Hieu
creators_id: hieupx@vnu.edu.vn
title: Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: Nowadays, users are much more willing to share their daily activities, their thoughts or feelings, and even their intentions (e.g., buy an apartment, rent a car, travel to somewhere, etc.) on online social media channels. Understanding intents of online users, therefore, has become a crucial need in many different business areas like production, finance/banking, real estate, tourism, e-commerce, and online marketing. In this paper, we will present our solutions to extract intent information from online social media texts. This can be seen as an information extraction or sequential segmentation task. In order to perform this task, we have built our machine learning models based on conditional random fields (CRFs), an advanced statistical graphical model for sequence data, and bidirectional long short–term memory (Bi–LSTM), a well-known deep learning model. To evaluate our methods, we have defined two intent extraction tasks in two domains: real estate and cosmetics & beauty. For each task, we have defined the tag set as well as prepared the labeled data set that consists of Vietnamese social media text posts with the annotation of intent words/phrases. Experimental results showed that the proposed methods can effectively extract intent information from online texts with significantly high accuracy.
date: 2017-10-19
date_type: published
official_url: http://kse2017.dhsphue.edu.vn
full_text_status: none
pres_type: paper
pagerange: 215-220
event_title: The 9th International Conference on Knowledge and Systems Engineering (KSE 2017)
event_location: Hue city, Vietnam
event_dates: October 19-21, 2017
event_type: conference
refereed: TRUE
related_url_url: http://ieeexplore.ieee.org/document/8119461/
related_url_type: pub
funders: Vietnam National University, Hanoi (VNU)
projects: VNU Project No. QG.15.29
citation:   Luong, Thai Le and Cao, Minh Son and Le, Duc Thang and Phan, Xuan Hieu  (2017) Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models.  In: The 9th International Conference on Knowledge and Systems Engineering (KSE 2017), October 19-21, 2017, Hue city, Vietnam.