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primary topic: Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models

Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models
type: AcademicArticle
type: Article
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
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository
subject: Information Technology (IT)

Minh Son Cao
type: Person
given name: Minh Son
family name: Cao

Thai Le Luong
type: Person
given name: Thai Le
family name: Luong

Xuan Hieu Phan
type: Person
given name: Xuan Hieu
family name: Phan

Duc Thang Le
type: Person
given name: Duc Thang
family name: Le

The 9th International Conference on Knowledge and Systems Engineering (KSE 2017)
type: Conference
type: Event

Hue city, Vietnam
type: SpatialThing

HTML Summary of #2594 Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models
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primary topic: Intent Extraction from Social Media Texts Using Sequential Segmentation and Deep Learning Models

Information Technology (IT)
type: Concept