Industrial Problem Solution
A word classification problem in which trade product names match numerical codes
2020-06-151. Company introduction
Korea Trade Information Communication is a company that provides trade automation services through the Internet and establishes electronic trade infrastructure for the automation of complex import and export processes.
2. Problem Background and Summary
Advanced algorithms to match product item names to numerical codes using textual information about products related to trade
Ask to explore mathematical methods of expressing item name words in numerical vectors and develop a deep learning model for text classification
3. Solving Process
Explain the process of tokenization, word embedding algorithms, which are necessary steps in quantifying item names, and deliver comparative analysis of the data.
Using embedded vectors of item names, various deep learning model networks matching to numerical codes were designed to calculate model accuracy.
4. Ripple effects and future plans
More than 80% accuracy was obtained in the prediction of top3 or top5 rather than single numerical code classification.
It will be used as a result to determine the feasibility of the project whether the trade-related product names can be matched to the number code.
It is expected to improve results by comparing and analyzing existing results by applying the recent Google-developed Bidirectional Encoder Presentations from Transformer (BERT).
1. Company introduction
Korea Trade Information Communication is a company that provides trade automation services through the Internet and establishes electronic trade infrastructure for the automation of complex import and export processes.
2. Problem Background and Summary
Advanced algorithms to match product item names to numerical codes using textual information about products related to trade
Ask to explore mathematical methods of expressing item name words in numerical vectors and develop a deep learning model for text classification
3. Solving Process
Explain the process of tokenization, word embedding algorithms, which are necessary steps in quantifying item names, and deliver comparative analysis of the data.
Using embedded vectors of item names, various deep learning model networks matching to numerical codes were designed to calculate model accuracy.
4. Ripple effects and future plans
More than 80% accuracy was obtained in the prediction of top3 or top5 rather than single numerical code classification.
It will be used as a result to determine the feasibility of the project whether the trade-related product names can be matched to the number code.
It is expected to improve results by comparing and analyzing existing results by applying the recent Google-developed Bidirectional Encoder Presentations from Transformer (BERT).