Contextual Deep Search using Long Short Term Memory Recurrent Neural Network

被引：0

作者：

Rahman, Mohammad Arifur ^{[1
]}

Ahmed, Fahad ^{[1
]}

Ali, Nafis ^{[2
]}

机构：

[1] Amer Int Univ Bangladesh, Dept Comp Sci, Dhaka, Bangladesh

[2] Univ Sydney, Fac Engn & Informat Technol, Sydney, NSW, Australia

来源：

2019 1ST INTERNATIONAL CONFERENCE ON ROBOTICS, ELECTRICAL AND SIGNAL PROCESSING TECHNIQUES (ICREST) | 2019年

关键词：

LSTM; RNN; Neural Network; POS Tagging; Normalization; Web Scraping; MEAN Stack;

D O I：

10.1109/icrest.2019.8644508

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The internet is teeming with an ocean worth of information and combing through all that in order to find what one wants can become a daunting task if one does not possess the right tools and techniques. This paper explores one such technique, exploiting the rapidly responsive Long-Short Term Memory Recurrent Neural Networks (LSTM-RNNs) by harnessing the machine learning capabilities of neural networks and eventually, provides contextual search results at the finger tips of the user of the system.

引用

页码：39 / 42

页数：4

共 14 条

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