My published paper in Information Processing and Management (IPM 2017)

My paper related to Text Classification was published in Information Processing and Management (SSCI & SCIE). The title is "How to Use Negative Class Information for Naive Bayes Classification" and Its abstract is as follows:

The Naive Bayes (NB) classifier is a popular classifier for text classification problems due to its simple, flexible framework and its reasonable performance. In this paper, we present how to effectively utilize negative class information to improve NB classification. As opposed to information retrieval, supervised learning based text classification already obtains class information, a negative class as well as a positive class, from a labeled training dataset. Since the negative class can also provide significant information to improve the NB classifier, the negative class information is applied to the NB classifier through two phases of indexing and class prediction tasks. As a result, the new classifier using the negative class information consistently performs better than the traditional multinomial NB classifier.

You can freely get the PDF version of this paper from the link https://authors.elsevier.com/a/1VSJt15hYdYMhA until September 15, 2017.

This is the fourth manuscript about text classification using negative class information: SIGIR 2012, Pattern Recognition Letters 2015, JASIST 2015 and IPM 2017. Actually, I'm still interested in this topic so I hope that I will be able to do more studies about that.

Text Classification and Summarization (Using Natural Language Processing and Machine Learning Techniques)

I gave an invited talk at KISTI. The title is "Text Classification and Summarization (Using Natural Language Processing and Machine Learning Techniques)."

http://web.donga.ac.kr/yjko/talks/TC&TS(Youngjoong%20Ko).pdf

How to Develop NLP Tools with DNN Techniques

I gave an invited talk in IT 21 Global Conference at June 2, 2017. The title is "How to develop NLP tools with DNN techniques."

http://web.donga.ac.kr/yjko/talks/NLP_Tools_with_DNN(Youngjoong%20Ko).pdf

The Basic Concept of TensorFlow

I am preparing to teach TensorFlow in my graduate course. TensorFlow is Google's open software library for machine learning. The first class is about the basic concept of TensorFlow.

http://web.donga.ac.kr/yjko/usefulthings/TensorFlow-Basic-Concept_Ko.pdf

Next topic is about "Practice of NNet with the MNIST data."

Spoken Language Understanding

One of my research areas is about Dialogues Systems. Nowadays, I am arranging  my work in this research into several ppt files. The following pdf file is the first summary of my work and its topic is Spoken Language Understanding (SLU).

http://web.donga.ac.kr/yjko/talks/SLU(Youngjoong Ko).pdf

I will try to post about Dialogue Modeling.

Multilayer Perceptron

This is my second ppt and Python code for studying Multilayer Perceptron (MLP) and the Back-propagation algorithm. Actually, this will be used in my Artificial Intelligent class.

Please check out the following links.

http://web.donga.ac.kr/yjko/usefulthings/Multilayer-Perceptron_Ko.pdf
http://web.donga.ac.kr/yjko/src/mlp.py

The next topic is "Introduction of Tensorflow."

Perceptron

Deep Learning becomes so popular these days, and people who want to study deep learning have to know Python, Perceptron, Multilayer Perceptron (MLP) and the Backpropagation algorithm. So I will make ppt materials including these topics. Actually, this will be used in my Artificial Intelligent class.

Please check out the following links.

This is the first ppt material for Perceptron and a Python code for practice.

http://web.donga.ac.kr/yjko/usefulthings/Perceptron_Ko.pdf
http://web.donga.ac.kr/yjko/src/perceptron.py

And you can start to study Python and Numpy from this:

http://cs231n.github.io/python-numpy-tutorial/ 

The next topic is Multilayer Perceptron.