Weakly Supervised Short Text Categorization Using World Knowledge

2020 ◽  
pp. 584-600
Author(s):  
Rima Türker ◽  
Lei Zhang ◽  
Mehwish Alam ◽  
Harald Sack
Keyword(s):  
Text Categorization ◽  
World Knowledge ◽  
Short Text ◽  
Weakly Supervised

scholarly journals Filtering and Classifying Relevant Short Text with a Few Seed Words

2019 ◽  
Vol 3 (3) ◽  
pp. 165-186 ◽  
Author(s):  
Chenliang Li ◽  
Shiqian Chen ◽  
Yan Qi
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
State Of The Art ◽  
Superior Performance ◽  
Support Vector ◽  
Short Text ◽  
Text Filtering ◽  
Supervised Classifiers ◽  
Real World Datasets ◽  
Weakly Supervised

Abstract Filtering out irrelevant documents and classifying the relevant ones into topical categories is a de facto task in many applications. However, supervised learning solutions require extravagant human efforts on document labeling. In this paper, we propose a novel seed-guided topic model for dataless short text classification and filtering, named SSCF. Without using any labeled documents, SSCF takes a few “seed words” for each category of interest, and conducts short text filtering and classification in a weakly supervised manner. To overcome the issues of data sparsity and imbalance, the short text collection is mapped to a collection of pseudodocuments, one for each word. SSCF infers two kinds of topics on pseudo-documents: category-topics and general-topics. Each category-topic is associated with one category of interest, covering the meaning of the latter. In SSCF, we devise a novel word relevance estimation process based on the seed words, for hidden topic inference. The dominating topic of a short text is identified through post inference and then used for filtering and classification. On two real-world datasets in two languages, experimental results show that our proposed SSCF consistently achieves better classification accuracy than state-of-the-art baselines. We also observe that SSCF can even achieve superior performance than the supervised classifiers supervised latent dirichlet allocation (sLDA) and support vector machine (SVM) on some testing tasks.

A Study on the Short Text Categorization using SNS Feature Informations

2016 ◽  
Vol 14 (6) ◽  
pp. 159 ◽  
Author(s):  
Sung-Hee Na ◽  
Jung-In Kim ◽  
Eun-Ji Lee ◽  
Pan-Koo Kim
Keyword(s):  
Text Categorization ◽  
Short Text

Filtering Dirty Words in Online Social Network by Applying Automated Filtering System

2014 ◽  
Vol 573 ◽  
pp. 560-564
Author(s):  
P. Kumari Bala ◽  
D. Jemi Florinabel ◽  
S. Sivasakthi
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Online Social Networks ◽  
Text Categorization ◽  
Online Social Network ◽  
Project Work ◽  
Rule Based ◽  
Short Text ◽  
Filter Algorithms ◽  
Content Based Filtering

The aim of the project work is automatically to filter the dirty words from other users without displaying to the profile owner. In Online Social Network may have possibilities of posting some dirty messages so it need to filter without displaying to owner. It has achieved by using Rule based Filtering System. The Rule Based Filtering System allows users customize to filter the noisy or dirty words by applying some filtering Criteria. It exploits Machine Learning (ML). Machine Learning is a text categorization techniques to specify some categories for assign the short text dirty words based on their content. The content-based filtering on messages posted on user space has specified the additional challenges to be given the short length of these messages. Online social networks not only make it easier for users to share their opinions with each other, but also serve as a platform for developing filter algorithms.

Semi-supervised short text categorization based on attribute selection

2010 ◽  
Vol 30 (4) ◽  
pp. 1015-1018 ◽  
Author(s):  
Yue-hong CAI ◽  
Qian ZHU ◽  
Ping SUN ◽  
Xian-yi CHENG
Keyword(s):  
Text Categorization ◽  
Attribute Selection ◽  
Short Text
Sign in / Sign up

Export Citation Format

Share Document