https://github.com/sarcode/company_acquisition_nlp
Text which contains a news about acquisition of a company , by a company and it predicts using Natural Language Processing about [ Acquired_Final ] & [ Acquired_Target ]
https://github.com/sarcode/company_acquisition_nlp
Last synced: 11 months ago
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Text which contains a news about acquisition of a company , by a company and it predicts using Natural Language Processing about [ Acquired_Final ] & [ Acquired_Target ]
- Host: GitHub
- URL: https://github.com/sarcode/company_acquisition_nlp
- Owner: SarCode
- Created: 2018-10-16T11:10:00.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-04-21T22:59:45.000Z (about 3 years ago)
- Last Synced: 2025-04-11T03:17:03.461Z (about 1 year ago)
- Language: Python
- Size: 1.35 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
[![LinkedIn][linkedin-shield]][linkedin-url]
# Company_Acquisition_NLP
Statement contains a news about acquisition of a company , by a company and it predicts using Natural Language Processing about [ Acquired_Final ] & [ Acquired_Target ]
# Standard Approach
Match unique id from corpus set and training set
Tokenizer splits data into small figments
Use tagger library which informs about nouns, verbs, adjectives etc.
Use parser library defines the dependency of words to each other
Match unique id from corpus set and test set and predict Acq_Final & Tar_Final according to result of above algorithm
# Our Approach! Let’s Discuss.
Tokenizer
Stemming
Tagger
Rule Based Approach
# Rule Based Approach
Proper Noun:
Buy: Token after buy is target
Sell: Token after sell is target
Sells: Token after sells is target
Sells to: Token after sells to is acquiring
# Additional Details
Locate:
Problem_Statement_ML_MnA.zip
in repository for dataset and description of dataset
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/sarthak-agarwal-dell/