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Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM
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DOI：10.1109/ACCESS.2022.3206539.png)
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Abstract
En 中文
Information extraction from e-commerce platform is a challenging task. Due to significant increase in number of ecommerce marketplaces, it is difficult to gain good accuracy by using existing data mining techniques to systematically extract key information. The first step toward recognizing e-commerce entities is to design an application that detects the entities from unstructured text, known as the Named Entity Recognition (NER) application. The previous NER solutions are specific for recognizing entities such as people, locations, and organizations in raw text, but they are limited in e-commerce domain. We proposed a Bi-directional LSTM with CNN model for detecting e-commerce entities. The proposed model represents rich and complex knowledge about entities and groups of entities about products sold on the dark web. Different experiments were conducted to compare state-of-the-art baselines. Our proposed approach achieves the best performance accuracy on the Dark Web dataset and Conll-2003. Results show good accuracy of 96.20% and 92.90% for the Dark Web dataset and the Conll-2003 dataset, which show good performance compared to other cutting-edge approaches.
Keywords:
Electronic commerce
Crawlers
Data mining
Task analysis
Deep learning
Information retrieval
Training data
Natural language processing
Convolutional neural networks
Dark Web
Name Entity Recognition
natural language processing
bidirectional LSTM
convolutional neural network
word embedding
HTML
e-commerce
dark-web
entities detection
marketplace