Paper PublishingAccurate Short Text Classification using Bi-LSTM
Objective
The Main objective of this project is to compare the performance of the models (accuracies) like Bi-LSTM, LSTM, RNN, BERT and SVM and proposes that our Bi-LSTM models outperforms all the other models in terms of accuracies.
Abstract
In this project we are using Bi-Directional long short-term memory (Bi-LSTM) model for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. Our proposed method outperforms the existing Machine Learning models with better performances
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE AND HARDWARE REQUIREMENTS:
Operating system : Windows 7 or 7+
Ram : 8 GB
Hard disc or SSD : More than 500 GB
Processor : Intel 3rd generation or high or Ryzen with 8 GB Ram
Software’s : Python 3.6 or high version, Visual studio, PyCharm.
Learning Outcomes
- What is Deep Learning?
- Abut Deep Learning algorithms.
- About RCNN.
- Knowledge on PyCharm Editor.
Related Projects
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
